Glasnik hemičara i tehnologa Bosne i Hercegovine … 47/Glasnik_47...Glasnik hemičara i tehnologa Bosne i Hercegovine Bulletin of the Chemists and Technologists of Bosnia and Herzegovina

Glasnik hemičara i tehnologa Bosne i Hercegovine

Bulletin of the Chemists and Technologists of Bosnia and Herzegovina

December 2016.

Prirodno-matematički fakultet Sarajevo Faculty of Science Sarajevo

Print ISSN: 0367-4444 Online ISSN: 2232-7266

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Glasnik hemičara i tehnologa Bosne i Hercegovine


December 2016.

Prirodno-matematički fakultet Sarajevo Faculty of Science Sarajevo


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REDAKCIJA / EDITORIAL BOARD Editor-In-Chief / Glavni i odgovorni urednik Fehim Korać

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CONTENT / SADRŽAJ

Editorial I ORIGINAL SCIENTIFIC ARTICLES Synthesis and characterization of Ru(III) complexes with thiosemicarbazide based ligands

1-6

Ljubijankić Nevzeta Tešević Vele Grgurić-Šipka Sanja Jadranin Milka Begić Sabina Buljubašić Lejla Markotić Ena Ljubijankić Sead

Na+-

N

O

N

ORu

NC

NH2

S

NC

H2N

S

Y

Y

Calcium analysis in bones during aging process 7-10 Šapčanin Aida Sofić Emin

Spectroscopic Investigations of Co(II) and Cu(II) Interaction with Imatinib Mesylate and Capecitabine

11-16

Cipurković Amira

Horozić Emir

Crnkić Aida

Marić Snježana

Ljubijankić Nevzeta




2016 Issue 47


Antimicrobial effects of garlic (allium sativum L.) 17-20 Strika Ilma Bašić Azra Halilović Namir

Heavy metals pollution in children playgrounds -an environmental modelling and statistical analysis

21-26

Šapčanin Aida Čakal Mirsada Ramić Emina Smajović Alisa Pehlić Ekrem

Synthesis and Characterization of Neutral Ru(III) Complexes Containing Schiff Bases and N-donor Heterocyclic Ligands

27-32

Begić Sabina Ljubijankić Nevzeta

The phosphate removal efficiency electrocoagulation wastewater using iron and aluminum electrodes

33-38

Đuričić, T. Malinović, B.N. Bijelić, D.


Mathematical modeling and simulation of the composting process in a pilot reactor 39-48 Papračanin Edisa Petric Ivan

Development and validation of the mathematical model for synthesis of maleic anhydride from n-butane in a fixed bed reactor

49-58

Petrić Ivan Karić Ervin

TECHNICAL PAPERS Nanosensors applications in agriculture and food industry 59-70 Omanović-Mikličanin Enisa Maksimović Mirjana

Instructions for authors 71 Sponsors 79

295300305310315320325330

0 200 400 600

Tem

pera

ture

, K

Time, h

modelexperiment


Editorial The first study, ten years after war activities, to report about the content of heavy metals and metalloides, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) found in samples of soils from playgrounds was realized in Sarajevo city. Due to the fact that children, the most vulnerable population, are in direct contact with surface soils, it has been recommended that children’s playgrounds should be given special consideration in this respect. The war activities during 1992. to 1995. period caused a variety of damages and contamination of urban land. During the last five years, the background pollution of Sarajevo has been mainly created by atmosferic impurities emitted from different sources. The major polluting source of pollutants such as the heavy metals and metalloides in Sarajevo are vehicles because of high traffic density and also because vehicles (passenger cars and commercial vehicles) below the Euro 3/4 standard are used. Furthermore, heating plants (using crude oil or natural gas) and domestic heating (using wood and coal) are the other sources of inorganic and organic pollutants. Unfortunately, there is still inadequate emission control and soil quality standards in practice. The goal of this research was to identify and quantify some inorganic and organic pollutants found in the soil of Sarajevo public playgrounds. Some of the consequences, ten years after the war, are evident in the collapse of monitoring programmes, in the severing of links between individuals working at universities and government officials, and in the physical destruction of records, including scientific data that had been painstakingly collected over a long period of time. There is currently little ongoing environmental monitoring at work. It is clear, however, that the consequences of the war for the environment have been different and geograficaly uneven. Findings of this study suggest that the contaminated playground soils in a war damaged area, as is the Sarajevo area, are still sources of heavy metals and metalloides, PAHs and PCBs contamination. While playgrounds soil characterization would provide an insight into major inorganic and organic pollutants speciation and bioavailability, attempts at environmental remediation of polluted soils would entail knowledge of the source of contamination, basic chemistry, and environmental and other associated children’s health risks caused by these pollutants. Results of this study may contribute to a more accurate health risk assesment of the soils, and may support planning safer and more sustainable urban playground areas; these findings could assist developers in planning projects for a more efficient use of land, or in studies assessing children’s health risks, or in studies concerning the soil contamination and related legislation which is not sufficiently regulated in Bosnia and Herzegovina. Editors

Synthesis and characterization of Ru(III) complexes with

thiosemicarbazide-based ligands

Ljubijankić N.1, Tešević V.

2, Grgurić-Šipka S.

2, Jadranin M.

3, Begić S.

1, Buljubašić L.

1,

Markotić E.1, Ljubijankić S.

4

1Faculty of Science, Department of Chemistry, Zmaja od Bosne 33-35, Sarajevo, Bosnia and Herzegovina

2Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, Belgrade, Serbia

3Center of Chemistry – Institute for Chemistry, Technology and Metallurgy, University of Belgrade, Studentski

trg 12-16, Belgrade, Serbia 4School of Health Studies,University of Bihać, Žegarska aleja bb, Bihać, Bosnia and Herzegovina

INTRODUCTION

Thiosemicarbazones have been the subject of many

studies because of their variable bonding modes,

promising biological implications and structural diversity

(Al-Amiery et al., 2011). Among the most examined

compounds of this group is certainly salicylaldehyde

thiosemicarbazone (Vojinović-Ješić et al., 2011). The

synthesis of the transition metal complexes with

thiosemicarbazone ligands has been receiving

considerable attention due to the potentially

chemotherapeutic properties of both ligands and

complexes as antitumor and antibacterial agents (Sampath

and Jayabalakrishnan, 2016). It was found that their

dibasic tridentate thiosemicarbazones with ONS donors

are of immense importance as they possess a wide

spectrum of medicinal properties (El-Bahnasawy et al.,

2014). Thiosemicarbazone derivatives are of particular

chemical and pharmacological importance for the

possession of a number of different biological activities:

anticancer, antiviral, antibacterial, antifungal activity, etc.

(Kumar et al., 2013). Pharmacological potential of

thiosemicarbazone as antitumor agent is one of the most

promising areas of its research (Chil J. 2013).

Salicylaldehyde thiosemicarbazone and its derivatives are

a group of flexible tridentate ONS donors capable of

stabilizing the upper and lower oxidation state of

transition metal ions (Leovac, et al., 1983).

Thiosemicarbazone complexes differs from the free

ligand with respect to their biological properties (Chandra

et al., 2014). Altough the thiosemicarbazone, obtained by




2016

47

1-6 UDC: __________________________

Original Scientific Paper

Article info Received: 15/11/2016 Accepted: 07/12/2016

Keywords: Thiosemicarbazones Thiosemicarbazide

Tridentate ligand

ONS donors

Ruthenium

Corresponding author:

Nevzeta Ljubijankić E-mail: [email protected]

Phone: 033 279 956

Abstract: Three Ru(III) complexes general formula Na[RuL2] (where L = dibasic tridentate

thiosemicarbazone ligands) have been synthesized by reacting RuCl3 with thiosemicarbazide-

based ligands with ONS donors. Ligands of general formula (X)N-NH-C(S)-NH2 were prepared

through the condensation reaction of salycilaldehide or its derivatives (X =

salicylaldehyde, 5-Cl-salicylaldehyde, 5-Br-salicylaldehyde) with thiosemicarbazide. Complexes

have been formulated and characterized by mass spectrometry and infrared spectroscopy. The

data showed the formation of a complex with a 1:2 metal:ligand stoichiometries. The ligands

coordinated as an ONS tridentate dianion through the oxygen atom of the deprotonated phenolic

OH-group, the azomethine nitrogen atom and the sulfur atom after deprotonation of the

thiosemicarbazide residue in its thiol form.

2 Ljubijankic et al.

the condensation process with o-hydroxyl carbonyl

compounds, show antibacterial activity, the metal

complexes often manifest effect of a larger order of

magnitude compared to the corresponding ligand (Ngan

et al., 2011). A particularly important feature is the

reduction of the cytotoxic action of thiosemicarbazone,

which may be reduced by complexing to the metal cation

(Rana et al., 2002). Synthesis of transition metal

complexes with thiosemicarbazone ligands attracted

considerable attention because of the potentially useful

chemotherapeutic properties and ligands and complex in

terms of anticancer and antibacterial activity (Al-Amiery

et al., 2012). Salicylaldehyde thiosemicarbazone is

usually expected to bind to a metal center, via

dissociation of two acidic protons, as a dianionic

tridentate ONS donor forming stable chelate (Datta et al.,

2011). Platinum group metal complexes with

thiosemicarbazones show a wide range of

pharmacological activity (Thangadurai et al., 2001). The

aim of this work was to study the syntheses, as well as the

spectral and structural characteristics of three Ru(III)

complex with salicylaldehyde thiosemicarbazone derived

from salicylaldehyde and Cl and Br substituted

salicylaldehyde and thiosemicarbazide.

EXPERIMENTAL

Materials and methods

Physical measurements

All chemicals used were commercially available with

analytical grade of purity and used without further

purification. Infrared spectra were recorded as KBr

pellets on a Perkin Elmer spectrum BX FTIR System in

region 4000 – 400 cm-1

. Agilent 6210 Time-of-Flight

LC/MS system (Agilent Technologies, Santa Clara,

California, USA) equipped with an ESI interface (ESI–

ToF–MS) was used for recording of mass spectra and for

the confirmation of molecular formulas of compoundas.

The ESI was operated in a negative mode and nitrogen

was used as the drying gas (12 L/min) and nebulizing gas

at 350°C (45 psi). The OCT RF voltage was set to 250 V

and the capillary voltage was set to 4.0 kV. The voltages

applied to the fragmentor and skimmer were 140 V and

60 V, respectively. Scanning was performed from 100 to

2000 m/z (mass-to-charge ratio). The identification of

compound was as follows: compound was dissolved in

the acetonitrile (concentration of 1 mg/mL), and a direct

injection of 5 μL sample was conducted by 1200 Series

HPLC (Agilent Technologies, Waldbronn, Germany)

without a separation column. The isocratic mobile phase

consisted of 50% acetonitrile and 50% of 0.2% formic

acid in water (v/v) at a flow rate of 0.2 mL/min. Using

these parameters, the anionic part of the compound was

detected as the corresponding [M]- ion.

Synthesis of ligands Thiosemicarbazide (0.01 mol; 0.91 g) was dissolved in

ethanol (60 mL) by refluxing at 50°C and ethanolic

solution (30 mL) of the salicylaldehyde or 5-Cl-

salicylaldehyde or 5-Br-salicylaldehyde (0.01 mol; 0.89

mL, 0.157 g, 0,201 g, respectively) was added. The

reaction mixture was refluxed for four hours at 60°C. The

volume of reaction mixture was reduced and then cooled

on ice water. The crystals of salicylaldehyde

thiosemicarbazone, 5-Cl-salicylaldehyde

thiosemicarbazone and 5-Br-salicylaldehyde

thiosemicarbazone, hereinafter HL1, HL2 and HL3,

respectively, were precipitated out. The crystals were

recrystallized in ethanol.

Synthesis of complexes Ethanolic solution (20 mL) of the thiosemicarbazone

ligand HL1 or HL2 or HL3 (1 mmol; 0.195 g or 0.229 g

or 0.273 g, respectively) was added to the solution of

RuCl3∙3H2O (0.5 mmol; 0.131 g) in ethanol (20 mL) and

the reaction mixture was refluxed for 4-5 hours. Volume

of the resulting solution was reduced to 10 mL at rotary

vacuum evaporator and the solution was left overnight.

The precipitation was performed by adding the equimolar

amounts of water solution of NaCl. The resulting

crystalline compound was filtered under suction, washed

with ethanol and ether, and dried in vacuum. Dark green

partly crystalline product is air stable, soluble in most

common polar organic solvents and insoluble in apolar

organic solvents and water. Yield: 63%

RESULTS AND DISCUSSION

Ligands HL1, HL2, HL3, general formula (X)N-NH-

C(S)-NH2 were prepared by mixing equimolar amounts of

thiosemicarbazide and salicylaldehyde or its derivatives

(X =salicylaldehyde, 5-Cl-salicylaldehyde, 5-Br-

salicylaldehyde) in absolute ethanol under reflux for

four hours at 60°C (Pahontu et al., 2013) (Figure1).

Figure 1. Preparation scheme for ligands L1, L2, L3; Y = H, Cl, Br,

respectively

The complexes Na[RuL2] (L = HL1, HL2, HL3) were

prepared by the modifed synthetic procedure (Kumar et

al., 2013) by mixing 1:2 molar ratio of RuCl3 and the

ligands in absolute ethanol (Figure 2).

Figure 2. Preparation scheme for complexes Na[Ru(HL1)2],

Na[Ru(HL2)2], Na[Ru(HL3)2] ; Y = H, Cl, Br, respectively

Na[Ru(LH1)2]

ESI-ToF MS m/z: [C16H14N6O2S2Ru]- 487.96567; FT-IR

HL1 (KBr, cm-1

): 1616 s [νs(C=N)], 1269 s [νs(C-Ophen)];

777 s [νs(C=S)], FT-IR Na[Ru(HL1)2] (KBr, cm-1

): 1605

s [νs(C=N)], 1280 s [νs(C-Ophen)], 723 s [νs(C-S), 587 s

[νs(Ru-N), 509 s [νs(Ru-O), 441 s [νs(Ru-S)]

Bulletin of the Chemists and Technologists of Bosnia and Herzegovina, 2017, 47, 1-6 3

Na[Ru(LH2)2]

ESI-ToF MS m/z: [C16H12N6O2S2Cl2Ru]- 555.88615; FT-

IR HL2 (KBr, cm-1

): 1611 s [νs(C=N)], 1280 s [νs(C-

Ophen)]; 777 s [νs(C=S)], FT-IR Na[Ru(HL2)2] (KBr, cm-

1): 1589 s [νs(C=N)], 1266 s [νs(C-Ophen)], 723 s [νs(C-S),

547 s [νs(Ru-N), 484 s [νs(Ru-O), 438 s [νs(Ru-S) ]

Na[Ru(LH3)2]

ESI-ToF MS m/z [C16H12N6O2S2Br2Ru]- 643.78526; FT-

IR HL3 (KBr, cm-1

): 1611 s [νs(C=N)], 1261 s [νs(C-

Ophen)]; 777 s [νs(C=S)], FT-IR Na[Ru(HL3)2] (KBr, cm-

1): 1595 s [νs(C=N)], 1278 s [νs(C-Ophen)], 723 s [νs(C-S),

544 s [νs(Ru-N), 506 s [νs(Ru-O), 438 s [νs(Ru-S) ]

ESI ToF MS mass spectrometry confirmed existence of

[C16H14N6O2S2Ru]-, [C16H12N6O2S2Cl2Ru]

- and

[C16H12N6O2S2Br2Ru]-

ions with m/z values at

487.96567, 555.88615 and 643.78526, respectively.

These ligands are coordinated to ruthenium center as a

ONS tridentate dianion through the oxygen atom of the

deprotonated phenolic OH-group, the azomethine

nitrogen atom and the sulfur atom after deprotonation of

the thiosemicarbazide.This was confirmed by IR spectra:

shift of azomethine stretching to lower frequencies, shift

of C-O(H) vibration to higher frequency and

disappearance of the vibration of C=S double bond in the

spectra of complexes (El-Bahnasawy et al., 2014).

In order to determine the mode of coordination the most

significant infrared spectral frequencies for the metal

complexes are compared with the free ligands. A band

observed at 1616, 1611 and 1611 cm-1

due to the

azomethine C=N stretching frequency of the free ligands

HL1, HL2 and HL3 respectively was shifted to lower

frequency in the spectra of the complexes at 1586 – 1605

cm-1

indicating the coordination through N atom.

Deprotonated phenolic oxygen – strong absorption band

in spectra of ligand positioned at 1261 – 1269 cm-1

after

coordination is shifted to 1278 – 1280 cm-1

(Baiu et al.,

2009), which corresponds to forming of weaker C-O(Ru)

bond comparing to C-O(H) and confirms coordination of

ligans to Ru(III) via deprotonated phenolic oxygen.

Also, in the IR spectra of ligands, HL1, HL2 and HL3,

the characteristic vibration of the (OH) band is observed

at 3444, 3410, 3454 cm-1

, respectively. The absence of

this band in the IR spectra of the complexes indicates the

coordination through the phenolic oxygen (Vojinović-

Ješić et al., 2011). The ligands showed band at 777 cm-1

for HL1, HL2 and HL2 for the vibration of the C=S

double bond (Wiles et al., 1967; Thangadurai et al.,

2001). The C=S band was disappeared in the complexes

(Al-Amiery et al., 2011) and a new band, C–S appeared at

723–743 cm-1

. This confirms that the other coordination

site to ruthenium is through thiolate sulphur (Sampath et

al., 2016). The appearance of bands in the spectra

complexes, Na[Ru(LH1)2], Na[Ru(LH2)2] and

Na[Ru(LH3)2] at 1636,1634, 1629 and 1616, 1588, 1599

cm-1

due to C=N-N=C (Bharti et al., 2013) and new C=N

bond, respectively, also indicates that Ru is bonded

through thiol sulphur (El Bahnasawy et al., 2014). The

medium intensity band in the region 544 –587 cm-1

is

attributed to Ru–N, in the region 484 – 509 cm-1

is

attributed to Ru–O, and in the region 438 – 448 cm-1

is

attributed to Ru–S bonds (Bahnasawy et al., 2014).

Figure. 3. Mass spestrum of Na[Ru(HL1)2]

Figure. 4. Proposed structure for the complexes


Table 1: ESI ToF MS data for molekular ions of compounds

Complex Molecular ion Ion Mass Measured

Mass

Error

(mDa)

Error

(ppm)

Na[Ru(HL1)2] [C16H14N6O2S2Ru]- 487.96686 487.96770 -1.18976 -2.44

Na[Ru(HL2)2] [C16H12N6O2S2Cl2Ru]- 555.88892 555.88615 -2.76566 -4.98

Na[Ru(HL3)2] [C16H12N6O2S2Br2Ru]- 643.78789 643.78526 -2.63104 -4.09

Figure 5. FT IR spectrum of ligand HL1

Figure. 6. FT IR spectrum of complex Na[Ru(HL1)2]

HL1

4000 400 3500 3000 2500 2000 1500 1000 500

101

1

10

20

30

40

50

60

70

80

90

cm-1

%T

777

1616

1269

3443

3321

1062,

Na[Ru(HL1)2]

2320 184 2000 1500 1000 500

104

-4 -0

10

20

30

40

50

60

70

80

90

100

cm-1

%T

1590

1606

1636

723

587

509

441

1280


Table 2: Characteristic vibrations of ligands and complexes

Figure 7. Comparative FT IR spectra of ligands HL1 and complex Na[Ru(HL1)2]

CONCLUSION

Three ruthenium(III) complexes of the type Na[RuL2]

(where L = dibasic tridentate thiosemicarbazone ligand),

have been synthesized. Complexes have been

synthesized and characterized by mass spectrometry and

infrared spectroscopy. The data showed the formation of

a complex with a 1:2 metal:ligand stoichiometries. The

ligands coordinated as an ONS tridentate dianion

through the oxygen atom of the deprotonated phenolic

OH-group, the azomethine nitrogen atom and the

thiolate sulfur sulfur atom after deprotonation of the

thiosemicarbazide. ESI ToF mass spectrometry

confirmed existence of [C16H14N6O2S2Ru]-,

[C16H12N6O2S2Cl2Ru]-

and [C16H12N6O2S2Br2Ru]-

ions

with m/z values at 487.96567, 555.88615 and 643.78526,

respectively.

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Ligand/complex ν(C=N),

cm-1

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cm-1

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cm-1

ν(Ru-N),

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HL2 1611 1266 777 - - - -

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HL3 1611 1261 777 - - - -

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HL1 Na[Ru(HL1)2]

2252 214 2000 1500 1000 500

102

-5 -0

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Polyhedron. 21 (2002), 1023-/1030.

Sampath K. and Jayabalakrishnan C. (2016).

Ruthenium(III) Thiosemicarbazone Complexes:

Synthesis, Characterization, DNA Binding,

Antibacterial, In vitro Anticancer and Antioxidant

Studies. DJ Journal of Engineering Chemistry and

Fuel, 1(1), 40-53.

Thangadurai T.D. and Natarajan K. (2001). Tridentate

Schiff base complexes of ruthenium(III) containing

ONS/ONO donor atoms and their biocidal activites.

Transition Metal Chemistry, 26, 717-722

Vojinović-Ješić LJ. S., Leovac V. M., Lalović M. M.,

Češljević V.I., Jovanović Lj. S., Rodić M. V. and

Divjaković V. (2011). Transition metal complexes

with thiosemicarbazide-based ligands. Part 58.

Synthesis, spectral and structural characterization of

dioxovanadium(V) complexes with salicyl aldehyde

thiosemicarbazone, J. Serb. Chem. Soc. 76 (6), 865–

877.

Wiles D.M., Gingras BA, Suprunchuk T. (1967). The C=

S stretching vibration in the infrared spectra of some

thiosemicarbazones. Canadian Journal of

Chemistry, 45(5), 469-473.

Summary/Sažetak

Sintetizirana su tri Ru(III) kompleksa opšte formule Na[RuL2] (L = dibazni tridentatni tiosemikarbazon ligand), reakcijom

RuCl3 sa ONS donorskim ligandima na bazi tiosemikarbazona. Ligandi opšte formule (X)N-NH-C(S)-NH2 pripremljeni su

reakcijom kondenzacije salicilaldehida i njegovih derivata (X = salicilaldehid, 5-Cl-salicilaldehid, 5-Br-salicilaldehid) sa

tiosemikarbazidom. Kompleksi su formulisani i karakterizirani ESI ToF masenom spektrometrijom i infracrvenom

spektroskopijom. Podaci pokazuju formiranje kompleksa sa 1:2 metal:ligand stehiometrijom. Ligandi su koordinirani kao

ONS tridentatni dianioni preko atoma kisika deprotonizirane OH-grupe, azometinskog atoma azota i atoma sumpora nakon

deprotonizacije ostatka tiosemikarbazida u njegovom tiolnom obliku.

Calcium Analysis in Bones During Aging Process

Sapcanin, A.a,

*, Sofic, E.a, b

aFaculty of Pharmacy, University of Sarajevo, Bosnia and Herzegovina

bFaculty of Science, University of Sarajevo, Bosnia and Herzegovina

INTRODUCTION

Bone is a specialized connective tissue that together with

cartilage makes up the skeletal system. These tissues serve

three functions: a) mechanical support and site of muscle

attachment for locomotion; b) protective: for vital organs

and bone marrow, and c) metabolic: reserve of ions for the

entire organism, especially calcium and phosphate. It is

also a composite structure, consisting of inorganic mineral

crystals an extracellular organic matrix, cells, lipids and

waters. The mineral crystals are analogous to the geologic

mineral hydroxyapatite (Boskey and Coleman, 2010). The

cells which produce, nurture and remodel the mineralized

extracellular matrix, also respond to mechanical and other

signals, which determine the properties (morphology and

function) of the bone (Boskey and Coleman, 2010).

Bone remodelling is a complex process which involves a

number of cellular functions directed toward the

coordinated resorption and formation of new bone

(Ronchetti et al., 1996; Goldhaber, 1997; Jin et al., 2000).

Catabolic agents (prostaglandine E2 (PGE2) and human

parathyroid hormone (h-PTH) fraction 1-34) or anabolic

agents (ascorbic acid (AA) and bone morphogenetic

proteine 4 (BMP4) could stimulate bone resorption or bone

formation directly in a bone organ-culture (Dempster et al.,

1993; Goldhaber, 1997; Sampath Kuber, 1999).

The main objective of the current research proposal is to

establish calcium as an independent index for observing

bone resorption and bone formation processes, which are

age dependant.

EXPERIMENTAL

Bone organ-culture system

Calvaria of five-day old mice ICR strain, were dissected

aseptically to encompass part of the frontal bone and most

of both parietal bones. Dulbecco’s Modified Eagle’s

Medium containing glucose, glutamine, bovine serum

albumin, fraction V, penicillin and streptomycin, were

added to each bone culture tube. This medium was serum

free. Catabolic or anabolic agents were included in the

medium. The bone culture tubes were incubated in a roller

aparatus for 7 or 14 days at 370 C and oxygenated with 50%

O2, 5% CO2 and 45% N2. The media were changed every 2-

3 days, and after each change of media the used medium

from each bone culture tube was analyzed individually for

calcium release from the bone into the medium. Bones were




2016

47

7-10 UDC: __________________________


Article info Received: 27/10/2016 Accepted: 06/12/2016

Keywords: calcium ion-selective electrode atomic absorption spectrophotometry (AAS)

resorption

aging

bones

*Corresponding author: E-mail: [email protected]

Phone: 00-387-33-586187

Fax: 00-000-00-0000000

Abstract: The resorption process in bone organ culture can be measured and evaluated by

only measuring the calcium concentration in the medium with calcium ion-selective

electrode. Reliable and consistent calcium resorption from bone using 500 ng/ml

prostaglandine E2 or 250 ng/ml human parathyroid hormone (1-34) have occurred. The

results, thus, indicate that calcium can be considered as an independent index of bone

resorption. Our preliminary measurements by atomic absorption spectrophotometry

(AAS), although statisticaly unrepresentative the sample – group being 9 babies and 9

adults, point to such conclusion. Bones were taken postmortem or post operationem.

Calcium concentration measured by AAS was at range of about 260 mg Ca/ mg ash in the

human baby bones (costae) and of about 430 mg Ca/mg ash in the human adults bones

(femur). Calcium amount measured also in the calvaria of five-day old mice ICR strain by

AAS. Values were at range of about 45 mg Ca/mg ash. AAS is a reference method for

calcium determination in human bones, however for simplification it is more appropriate

to use calcium ion-selective electrode.


8 Sapcanin et al.

fixed with formalin and processed for histological

examination when the experiment was terminated.

Calcium determination

A Varian spektra AA-10 Atomic Absorption

Spectrophotometer for calcium determination in human

bones was used. The calcium determination was carried out

at the 422 nm line; the light source was a hollow cathode

lamp. Weighed bone ash samples were hydrolyzed in 6 M

HCl. Used media from the bone organ – cultures were

analyzed individually for calcium content using Nova

Biomedical Calcium Analyzer, Model 7- Ca2+

ion selective

electrode (Nova Biochemical, Waltham, MA) according to

the instructions of the manufacturer (Yoon et al., 2000).

The amount of calcium measured from the bone organ-

culture medium after PGE2 or h-PTH fraction 1-34 was

included in the medium.

Analysis of hydroxyproline

Bones without fixing with formalin were hydrolyzed and

analyzed for hydroxyproline, using HPLC with

fluoroscence detection -Pico Tag method -Waters Division

of Millipore (Feste, 1992).

Statistical analysis of data

All data were subjected to a one-way analysis of variance

(ANOVA) using Fisher’s LSD test.


This work is performed to show that calcium analysis is

useful for observation and definition of complex

biochemical and morphological processes of bone

resorption and bone formation. The biological processes of

human growth and ageing were reflected in the amounts of

calcium in the bones (Boskey and Coleman, 2010)..

Our preliminary measurements by atomic absorption

spectrophotometry (AAS), although statistically

unrepresentative the sample-group being 9 babies and 9

adults, points to such conclusion. Calcium concentration

measured by AAS was in a range of approximately 260 g

Ca / mg ash in the human baby bones and of approximately

430 g Ca / mg ash in the human adults bones. Amounts of

calcium were also measured in the calvaria of five-day old

mice ICR strain by AAS. The values were in the range of

aapproximately 45 g Ca / mg calvaria.

Before proceeding with experiments designed to test the

agents in our “remodelling system” (Goldhaber, 1997) it

was necessary to establish a bone organ culture system that

would respond reliably and consistently to bone resorption

stimulating agents, such PGE2 or h-PTH fraction 1-34, so

that differences in the amount of resorption could be

determined more quantitatively by measuring the amount of

calcium released into the medium when the “used” medium

was replaced with fresh medium and at the time when the

experiment was terminated. Therefore, during the 7-day

culture period, therefore, calcium analysis of “used” media

was done three times, on day 2 or 3, day 5 and day 7.

From Figure 1. it may be seen that control bones (lacking

PGE2 in the medium) showed little, if any, resorption on

gross examination after 7 days in culture. On the other

hand, when PGE2 (500 ng/ml) was added to the culture

medium, gross resorption, distortion, and collapse of the

calvaria occured (Figure 2.). Similar experiments with

different concentrations of PTH revealed that 250 ng/ml of

h-PTH gave an adequate bone resorption response.

Figure 1. Control group. Picture of calvaria after 7 days of culture.

Note intact calvaria

Figure 2. Experimental group. PGE2 was added as a bone resorption

stimulating agent. Note bone resorption

Addition of 500 ng/ml PGE2 into the medium resulted in

increased calcium release and was statistically significant

(P<0.01) Figure 3. We observed that calcium release did

not occur in the control bones group and group with

addition of AA 150µg/ml. Inhibition of increased calcium

release occurred when AA was added into the medium with

PGE2.

Figure 3. Effect of AA, PGE2 and combination on calcium release

from the bone into the medium, during 7 day culture period.

UC -untreated controls; AA -group treated with AA 150µg/ml; PGE2-

group treated with PGE2 500 ng/ml; AA + PGE2 – group treated with

combination of AA 150µg/ml and PGE2 500 ng/ml; ** P<0.01

(ANOVA)

-5

0

5

10

15

ca

lciu

m r

ele

as

e (

mg

%)

AA UC PGE2 AA+PGE2


The addition of AA leads to good new osteoid formation

during the culture period.

Biogravimetry of calvaria treated with AA 150µg/ml

showed significant increase of calvarial weight (about 50

%) Figure 4.

-80

-60

-40

-20

0

20

40

60

80

1 5 9 13 17

% c

han

ge i

n w

eig

ht

aft

er

14 d

ays o

f cu

ltu

re

Figure 4. Effect of AA on the weight change of calvaria after 14 day

culture period.

150µg/ml AA was added into the medium to stimulate bone

formation and biosynthesis of collagen. HPLC analysis of

hydroxyproline (“biomarker” for collagen synthesis)

approved process of accelerated collagen synthesis and

significant increase of hydroxyproline amount compared

with untreated control bones (Figure 5.).

Figure 5. Effect of AA on hydroxyproline amount.

AA – calvaria treated with AA 150µg/ml; UC –untreated controls

after 7 day culture period.

The completed experiments confirmed calcium as an

independent index of bone resorption and also, as an

important parameter in the estimation of bone formation.

CONCLUSION

1. Only with a large number of bone samples would it be

possible to draw extrapolation curves reflecting the

relationship between the presentation bones calcium

amount and human age using AAS.

2. Reliable and consistent calcium resorption from bone

using 500 ng /ml PGE2 or 250 ng /ml h-PTH (1-34) have

occured. The results, thus, indicate that calcium can be

considered as an independent index of bone resorption.

3. Reliable and consistent bone formation in bone organ

culture using ascorbic acid 150 g /ml or after addition 50

ng /ml of bone morphogenetic protein 4 have been

stimulated. Both supstances stimulate osteogenesis. In this

case, increased calcium release into medium did not occur.

4. The resorption process in bone organ-culture can be

measured and evaluated only by measuring the calcium

concentration amount in the medium by calcium ion-

selective electrode. Further analysis (for example,

histological) is not required.

5. Measuring of calcium release from the bone into the

medium using a calcium ion-selective electrode is

insufficient for evaluation of bone formation process.

Analysis of hydroxyproline by HPLC with fluoroscence

detection and histological examination of osteoides are

necessary.

6. AAS is a reference method for calcium determination in

human bones, however for simplification it is more

appropriate to use calcium ion-selective electrode.

REFERENCES

Boskey, A., L., Coleman, R. (2010). Aging and Bone. J.

Dent. Res., 89, (12), 1333-1348.

Dempster, D., W., Cosman, F., Parisien, M., Shen, V.,

Lindsay, R. (1993). Anabolic actions of

parathyroid hormone on bone. Endocr. Rev., 14,

(16), 690-709.

Feste, A., S. (1992). Reversed-phase chromatography of

phenylthiocarbamyl amino acids: an evaluation

and a comparison with analysis by ion-exchange

chromatography. J. Chromatogr., 574,(1), 23-34.

Goldhaber, P. (1997). Spying on the osteoclast before,

during and after bone resorption in tissue culture:

In Davidovitch Z, Mah J (ed) Biological

Mechanisms of Tooth Eruption and Replacement

by Implants. Harvard Society for the advancement

of Orthodontics, Boston, MA, USA, pp 199-211.

Jin, L., Briggs, S., Chandrasekhar, S., Chirgadze, N.,

Clawson, D., Schevitz, R., Smiley, D., Tashjian,

A., Zhang, F. (2000). Crystal structure of Human

Parathyroid Hormone 1-34 at 0,9-A Resolution. J.

Biol. Chem., 275, (35), 27238-27244.

Ronchetti, I., Quaglino, D., Bergamini, G. (1996). Ascorbic

acid and connective tissue. In Robin Haris (ed)

Biochemistry and Biomedical Cell Biology, vol

25. Subcellular Biochemistry. Plenum press, New

York, pp 249-259.

Sampath, Kuber, T. (1999). Biology of Bone

Morphogenetic Proteins - An Overview. Bone, 24,

(4), 409-431.

Yoon, H.,J., Shin, J.,H., Lee, S.,D., Nam, H., Cha, G.,S.,

Strong, T.,D., Brown, R.,B. (2000). Solid-state ion

sensors with liquid junction-free polymer

membrane-based reference electrode for blood

analysis. Sensors and Actuators., B 64, 8-14.

0

100

200

300

400

500

600

Hyd

rox

yp

roli

ne n

mo

l/calv

ari

a

AA UC

Summary/Sažetak

Resorpcijski proces u organ kulturi kosti može se mjeriti i evaluirati mjerenjem koncentracije kalcija u mediju kalcijevom

jon selektivnom elektrodom. Upotreba 500 ng/ml prostaglandina E2 ili 250 ng/ml humanog paratiroidnog hormona (1-34)

ostvaruje pouzdan i konzistentan resorpcijski process. Stoga rezultati indiciraju da kalcij služi kao neovisni indicator

koštane resorpcije. Naša preliminarna mjerenja atomskom apsorpcionom spektrofotometrijom (AAS), statistički

nereprezentativnih uzoraka od 9 beba i 9 odraslih poentiraju takav zaključak. Kosti su uzete postmortem ili post

operationem. Koncentracija kalcija mjerena AAS kretala se od 260 mg Ca/ mg pepela u humanim bebi kostima (costae) do

430 mg Ca/mg pepela u humanism kostima odraslih (femur). Količina kalcija mjerena je AAS takođe i u kalvariji 5 dana

starog miša ICR vrste. Vrijednosti su se kretale oko 45 mg Ca/mg pepela. AAS je referentna metoda za mjerenje

koncentracije kalcija u humanim kostima, iako je radi pojednostavljenja prikladnija upotreba kalcijeve jon selektivne

elektrode.

Spectroscopic Investigations of Co(II) and Cu(II) Interaction with

Imatinib Mesylate and Capecitabine

Cipurković A.a, Horozić E.

a, Crnkić A.

a, Marić S.

a, Ljubijankić N.

b

aDepartment of Chemistry, Faculty of Science, University of Tuzla, Univerzitetska 4, 75 000 Tuzla, B&H

bDepartment of Chemistry, Faculty of Science, University of Sarajevo, Zmaja od Bosne 33-35, 71000 Sarajevo, B&H

INTRODUCTION

Cobalt and copper are present as trace elements in

biological systems and are very important for the activity

of many enzymes with different functions in human body.

Biological functions of these metal ions derive from

possible interaction of Co(II) and Cu(II) ions with O, N

and S donor atoms of various ligands and biomolecules in

human organism. (Krstić et al., 2015). Capecitabine

(CPC) is an orally-administered chemotherapeutic agent

used in the treatment of metastatic breast, stomach,

pancreas and colorectal cancers (Kathiravan and Pandey,

2014). CPC (5-deoxy-5-flouro-N-[(pentyloxy)carbonyl]-

cytidine), is fluoropyrimidine carbamate pro-drug of 5-

fluorouracil (5-FU) and its absorption is higher than 5-FU

(Kandimalla and Nagavalli, 2012; Sunkara et al., 2013).

Structure of CPC is presented at Figure 1.

Figure 1. Structure of Capecitabine




2016

47

11-16 UDC: __________________________

Original Scientific Article

Article info

Received: 15/11/2016

Accepted: 18/12/2016

Keywords:

Copper,

Cobalt,

FTIR,

UV spectroscopy,

Microscopic analysis

*Corresponding author:

Amira Cipurković

E-mail: [email protected]

Phone: + 387 35 320 753

Abstract: Cobalt and copper are present as trace elements in biological systems and they

are very important for the activity of many enzymes with different functions in the body.

Their biological functions derive from the possibility of potential interaction of their M(II)

ions with O, N and S donor atoms of various ligands and biomolecules in the living

organisam. Capecitabine and imatinib mesylate (ImM) are synthetic organic compounds

which are used in a treatment of some oncological diseases thus disturbing homeostasis of

biological system. In this study, UV and FTIR spectroscopic methods are used to

investigate metalligand interactions and products of their interaction at physiological

conditions using model test systems. FTIR spectrum of Co(II)-capecitabine model systems

show lack of absorption bands characteristic for -OH (at 3230 cm-1) and C=O groups

positioned at pyrimidine cycle (at 1718 cm-1) for pure capecitabine. It indicates interaction

of Co(II) ion with capecitabine via O-donor atoms. FTIR spectrum of pure ImM deviates

from spectrum of Co(II)-ImM system at 1250-1050 cm-1 wavelength region. This region

corresponds to peaks characteristic for mesylate ions (O3S-CH3), which indicates on

interaction between Co(II) and donor atoms containing molecule ligands (O and/or S).

UV results for model systems of M(II) with capecitabine and ImM show similar

absorption bands as those of pure ligand, while absobances are different (except for

Cu(II)-ImM). Since these investigations are done at approximately at physiological

conditions, it is expected that, after application of these ligands as pharmacological agents,

the same interactions are happening in the human body.


12 Cipurkovic et al.

Imatinib mesylate (ImM) is a tyrosine kinase inhibitor,

that was found to be one of the most recent drugs used for

the treatment of chronic myeloid leukemia and gastro-

intestinal stromal tumor (Zaharieva et al., 2013).

Structure of ImM is presented at Figure 2.

Figure 2. Structure of Imatinib Mesylate

Complexes of Cu(II) and Co(II) with these reagents were

synthesized and characterized.

EXPERIMENTAL

Materials and methods

Modeling System for UV spectroscopy: In the case of

testing interactions of M(II) ions with natural and

synthetic ligands, distilled water and ethanol-water

solutions were used as solvents in ratios between 50/50

and 80/20 (v/v.) Concentrations of metal and ligand

solutions were in order of 10-6

molL-1

. After stirring

samples in 1: 1 volume ratio, the same have being left to

stand for about one hour. After that, absorption spectra of

prepared solutions were recorded on Perkin Elmer λ25

UV/Vis spectrophotometer, in the wavelength region of

200-400 nm at room temperature (T = 25°C) in the quartz

cuvette, the optical path length l = 1.0 cm, with deuterium

lamp as a source of light. Measurements were performed

in the laboratory of Department of Physical Chemistry

and Electrochemistry at Faculty of Technology,

University of Tuzla.

Modeling System for FTIR spectroscopy: Metal and

ligand were mixed in 1: 2 (n/n) molar ratio. Solution of

dissolved metal (15 mL) and solution of ligand (15 mL)

were mixed in a beaker and stirred with a magnetic stirrer

without heating, with the appropriate adjustments of pH

values in solutions. pH-values were measured on Mettler

Toledo MP 220 pH-meter. In order to extract solid

products of M-L interactions, stock solutions were

prepared by adjusting pH values after stirring for 30

minutes and then left in a dark area for three days at room

temperature. After that, if precipitation occurred*, solid

phases were separated from solutions by filtration on

filter paper (blue ribbon) and thereafter dried in an oven

at temperatures between 40-50 °C. Isolated solid products

of M-L interactions were prepared as KBr pellet to record

FTIR spectra on FTIR Spectrum BX, Perkin Elmer, in

wavelength region 450-4000 cm-1

, resolution of 2 cm-1

at

room temperature (in Laboratory of Faculty of Pharmacy,

University of Tuzla).

* Because of the extremely small quantities of separated

precipitates in the case of model systems Cu(II)-CPC and

Cu(II)-ImM, the same could not be subjected to FTIR

analysis.


UV spectroscopic characterization

UV spectra of pure CPC and Co(II)-CPC model system

are showed at Figure 3. UV spectrum of CPC shows

several absorption maxima at 214, ~ 240 and 305 nm,

which completely corresponds to literature data

(Piórkowska, 2014). The binary system of Co(II)-CPC

shows maximum peak at low absorbance values. There is

hypo-chromic shift in comparison to the spectrum of pure

CPC, in which the last peak (with the highest λmax) is not

clearly defined as in the case of pure CPC. It can be

concluded that the Co(II) ions interacted with the CPC.

Calculated value of energy splitting for Co(II)-CPC

system is 559 kJmol-1

.

Figure 3. UV spectra of CPC and model system Co(II)-CPC

Bulletin of the Chemists and Technologists of Bosnia and Herzegovina 2016, 47, 11-16 13

UV spectra of pure ImM and Co(II)-ImM model system are

showed at Figure 4. ImM spectrum shows an absorption

maximum at about 255 nm which corresponds to the

literature data (Kumar Raja, 2010). The product of

interaction Co(II)-ImM gives new hypo-chromic shifted

spectrum with absorption maximum slightly dislocated

toward longer wavelengths at ~ 258 nm. Calculated value of

energy splitting for Co(II)-ImM system is 464 kJmol-1

.

Figure 4. UV spectra of ImM and model system Co(II)-ImM

UV spectra of pure CPC and Cu(II)-CPC model system

are showed at Figure 5. Spectrum of CPC and spectrum

of Cu(II)-CPC binary system have very similar profiles of

absorption bands, but different in the absorbance

intensity. Significant absorption values for model system

Cu(II)-CPC are less comparable to that of pure CPC.

Spectra of Cu(II) and Cu(II)-CPC are almost identical,

but with a very small deviations of the absorbance values.

The value of energy d-splitting is also 559 kJmol-1

.

Figure 5. UV spectrum of CPC and model system Cu(II)-CPC

UV spectra of pure ImM and Cu(II)-ImM model system

are showed at Figure 6. Spectrum of pure ImM and

spectrum of binary system Cu(II)-ImM shows

significantly different absorption bands with a positive

difference from 200 to 255 nm and a negative difference

from 255 to ~ 286 nm, and again positive difference to

400 nm.

Absorption band of model system shows hyper-chromic

shift, with absorption maximum shifted towards smaller

wavelengths. Based on the absorption maximum at about

216 nm, calculated energy splitting value is 554 kJmol-1


Figure 6. UV spectrum of ImM and model system Cu(II)-ImM

The spectrum of pure ImM and the spectrum of binary

system Cu(II)-ImM shows significantly different

absorption bands with a positive difference at 200 - 255

nm and negative difference at 255 to ~ 286 nm, and after

that positive difference to 400 nm. Absorption band of the

model system shows hyper-chromic shift, with the

absorption maximum shifted towards smaller

wavelengths. Based on the absorption maximum at about

216 nm, calculated energy splitting value is 554 kJmol-1

.

FTIR spectroscopic characterization

FTIR spectra of pure CPC and Co(II)-CPC model system

are showed at Figure 7. Comparison of these FTIR

spectra clearly shows a number of differences which

indicates on the interaction of Co(II) ion with CPC. Wide,

medium sharp peak at 3230 cm-1

in CPC, characteristic

for free -OH group is not visible at FTIR spectrum of

Co(II)-CPC model system indicating on a link between

oxygen atom as electron-donor and Co(II) ion. Very

intense peak at 1718 cm-1

visible at CPC spectrum

corresponds to the carbonyl group positioned on

pyrimidine ring. This peak is not visible for Co(II)-CPC

model system which points on the interaction between

metal ion and oxygen atom, which could cause

delocalization of electron pair of double bond to oxygen.

Figure 7. FTIR spectrum of pure CPC and model system Co(II)-CPC

Peaks visible at CPC spectrum at the 1000-1600 cm-1

wavelengths range obviously change because of the

interaction of Co(II) ion and ligand so the most of peaks

are lost or changed intensity (peaks of high intensity

become less pronounced and stretched).


Very intensive peak at 1647 cm-1

which corresponds to

the C = N group of CPC (Sunkara, 2013) is also not

visible in Co(II)-CPC system suggesting on some changes

in the structure of pyrimidine ring after chemical reaction.

FTIR spectra of pure ImM and Co(II)-ImM model system

are showed at Figure 8.

Figure 8.

FTIR spectrum

of pure

ImM and model system Co(II)-ImM

FTIR spectrum of pure ImM is very complicated. There

is an intensive series of bands, especially at wavelengths

<1700 cm-1

. On the other hand, FTIR spectrum of model

system Co(II) - ImM differs from the spectrum of pure

ImM, indicating the interaction of ions with this ligand.

The greatest changes in these spectra are observed in the

areas of 3200-2800 cm-1

and 1300-1000 cm-1

. The

spectrum of pure ImM and Co(II)-ImM system at 3258

cm-1

is characterized by a medium intense peak of

secondary amino-group. Very intense peak in the

spectrum of ImM visible at about 1660 cm-1

which

corresponds to a C = O stretching vibration of amide

group, is shifted toward lower values in the spectrum of

complex. Several more intense peaks of pure ImM in the

area of 1450-1200 cm-1

are characteristic of SO2 groups

of mesylate ion. These peaks are not clearly visible at the

spectrum of model system Co(II)-ImM, which shows

possible interactions of Co(II) ions with O- or S- donor

atoms of mesylate ion. Aromatic amine moiety of ImM

molecule shows C N vibration stretching in the area of

1342-1266 cm-1

which is caused by the increase of the

force in C N connections due to the resonance in the

ring.

Microscopic characterization of solid products of M-

L interaction

Microscope images of pure ligands and their solid

products with Co(II)-ions are showed at Figure 9.

Comparing microscopic images of ImM and the product

of its interaction with Co(II) ions, it can be observed that

there are differences in sizes and colors of their crystals.

Crystals of pure ImM are not clearly visible, while the

product of Co(II)-ImM interaction is characterized by

clearly defined crystals, predominantly pink color, which

indicates the presence of Co(II) ions. Ligand CPC and

Co(II)-CPC interaction product are of fine-grounded

crystal structure, with visible differences between

crystals (crystals of product are clearly visible).

CONCLUSION

The results obtained by UV and FTIR spectroscopy

clearly indicated on chemical interactions between metal

ions, Co(II) and Cu(II), and investigated ligands,

capecitabine and imatinib mesylate. These interactions

were probably achieved via O-donor atoms of both

ligands. Microscopic images confirmed the results

obtained with spectroscopic methods. There are

noticeable differences in sizes and colors of crystals.

Biological activities and cytotoxicity of these

compounds have not been thoroughly investigated yet.

REFERENCES

Kandimalla R. and Nagavally D. (2012). Validated

estimation of Capecitabine by UV-spectroscopic, RP-

HPLC and HPTLC method. International Research

Journal of Pharmacy, 3 (11), 163-166.

Kathiravan P. and Pandey V. P. (2014). Selection of

excipients for polymer coated capsule of

Capecitabine through drug-excipient compatability

testing. International Journal of PharmTech

Research, 6 (5), 1633-1639.


Krstić N., Nikolić R., Stanković M., Nikolić N. and

Đorđević D. (2015). Coordination Compounds of

M(II) Biometal Ions with Acid-Type Anti-

inflammatory Drugs as Ligands – A Review.

Tropical Journal of Pharmaceutical Research

February, 14 (2), 337-349.

Kumar Raja J., Sundar V. D., Magesh A. R., Nandha

Kumar S., Dhanaraju M. D. (2010). Validated

spectrophotometric estimation of imatinib mesylate

in pure and tablet dosage form. International Journal

of Pharmacy & Technology, 2 (3), 490-495.

Piórkowska E., Kaza M., Fitatiuk J., Szlaska I., Pawinski

T., Rudzki P. J. (2014). Rapid and simplified HPLC-

UV method with on-line wavelengths switching for

determination of capecitabine in human plasma.

Pharmazie 69, 500–505.

Sunkara S., Sravanthi D., Maheswari K. M., Salma S.

and Nalluri B.N. (2013). Development of modified

release tablet dosage forms of capecitabine for better

therapeutic efficacy. Journal of Chemical and

Pharmaceutical Research, 5 (1), 320-328.

Zaharieva M. M., Amudov G., Konstantinov S. M.,

Guenova M. L. (2013). Leukemia (book), Chapter 7 -

ModernTherapy of Chronic Myeloid Leukemia. (p.p.

228-244). INTECH.

Summary/Sažetak

Kobalt i bakar su kao elementi u tragu prisutni u biološkim sistemima i veoma su značajni za aktivnost mnogih enzima sa

različitim funkcijama u tijelu. Njihova biološka funkcija proističe iz potencijalne interakcije njihovih dvovalentnih iona sa

O, N i S donorskim atomima različitih liganda i biomolekula u organizmu. Kapecitabin i imatinib mesilat su sintetski

organski spojevi koji se koriste u tretmanu nekih onkoloških bolesti i pri tome narušavaju homeostazu biološkog sistema.

U ovom radu korištene su UV i FTIR spektroskopske metode za ispitivanje metal-ligand interakcija i produkata njihove

interakcije pri fiziološkim uslovima, korištenjem modelnih test sistema. FTIR spektar model sistema Co(II)-kapecitabin

pokazuje izostanak apsorpcionih traka karakterističnih za -OH (na 3230 cm-1) i C=O (na 1718 cm-1) grupe smještene na

pirimidinskom prstenu čistog kapecitabina. To ukazuje na interakciju Co(II) iona sa kapecitabinom preko O-donorskih

atoma. FTIR spektar čistog ImM razlikuje se od spektra model sistema Co(II)-ImM u oblasti talasnih dužina od 1250 do

1050 cm-1. Ovo područje odgovara pikovima karakterističnim za mesilatne ione (O3S-CH3), što ukazuje na interakciju

između Co(II) i atoma donora koje sadrže molekule liganda (O i/ili S). UV rezultati za model sisteme M(II) sa CPC i ImM

pokazuju slične apsorpcione trake kao i čisti ligandi, dok su apsorbance različite (osim za Cu(II)-ImM). S obzirom da su

ova istraživanja izvedena pri približno fiziološkim uslovima, može se očekivati da će se nakon primjene ovih liganada kao

farmakoloških reagenasa iste interakcije odvijati u ljudskom tijelu.

Antimicrobial effects of garlic (Allium sativum L.)

Strika, I.a, Bašić, A.

b, Halilović, N.

b

aUniversity of Sarajevo, Faculty of Science, Department of Biology, Zmaja od Bosne 33-35, 71000 Sarajevo, Bosnia and

Herzegovina aUniversity of Sarajevo, Faculty of Science, Department of Chemistry, Zmaja od Bosne 33-35, 71000 Sarajevo, Bosnia

and Herzegovina

INTRODUCTION

Garlic (Allium sativum L.) is a plant from the family of

arcs (Aliaceae). It is a herbaceous plant with height of

20-40 cm, a bulb of strong odour and pungent taste.

Sulphur compounds in garlic are responsible both for its

strong smell, and for its medicinal properties. The

undamaged plant contains alliin (S-allyl-L-cysteine

sulfoxide). Allin is a soluble, crystal, odourless

compound. It is a cysteine derivative and has

antimicrobial properties. Standardized garlic powder

contains 1.3% of allin. Slight damage causes changes in

alliin, which is broken down under the influence of

enzymes allinase into lactic acid and 2-propenyl-sulfonic

acid. This acid instantly dimerizes and builds allicin -

diallyl sulfate or diallyl disulfide. Allicin was first

isolated in 1940 and has been shown to have

antimicrobial activity against many viruses, bacteria,

fungi and parasites. Allicin produces diallyl sulfide, the

most important volatile compound of garlic and gives it

its characteristic smell. Garlic acts as an antibiotic,

antiseptic, antitoxic, antiviral, bactericide, carminative,

hipoholesterolemik, depurative, diuretic, expectorant,

fungicide, hypoglycemic, hipotensiv, and stomachic. It is

used in the food and pharmaceutical industries

(Agarwall, 1996). Given that garlic contains a variety of

biologically active substances and at the same time acts

as an antibiotic and fungicide, objectives of this study

were to examine the in vitro antimicrobial activity of

both fresh and thermally processed domestic and

imported garlic onto selected representatives of Gram-

positive and Gram-negative bacteria and fungus Candida

albicans, compare the antimicrobial effectiveness of heat

treated and fresh domestic and imported garlic and

assess the degree of sensitivity of selected

microorganisms to the effect of garlic.

MATERIALS AND METHODS

This study examined the antimicrobial activity of both

fresh, and heat treated Allium sativum as well as the

garlic of domestic origin (Kakanj) and the imported

(China) garlic. The study used three Gram-positive

bacteria: Staphylococcus aureus, methicillin-resistant

Staphylococcus aures (MRSA), and Bacillus subtilis, two

Gram-negative bacteria: Escherichia coli, Salmonella




2017

47

17-20 UDC: __________________________


Article info Received: 16/10/2016


Keywords:

Allium sativum

antimicrobial activity

fresh local garlic


Phone: 00-387-62-182670

Abstract: Garlic has been used as a source of food and medicine for thousands of years.

Given that the garlic contains different biologically active materials and acts as an

antibiotic and a fungicide, the purpose of this research was to estimate the degree of

sensitivity of three different Gram-positive bacteria: Staphylococcus aureus, methicillin-

resistent Staphylococcus aures (MRSA) and Bacillus subtilis; two types of Gram-negative

bacteria: Escherichia coli and Salmonella enteritidis; as well as the fungus Candida

albicans. The degree of sensitivity of tested microbes with regards to the agency of fresh

and thermally processed local and imported garlic was determined using the disc-diffusion

method. Examined antimicrobial-test substances exhibited antibacterial effect on all tested

gram-positive bacteria and gram-negative bacteria, as well as the fungistatic agency upon

fungus C. albicans. The strongest antimicrobial effect on all tested species was exhibited

by fresh local garlic. Preparates based on A. sativum could be introduced in clinical

practice for the treatment of infections caused by C. albicans.


18 Strika et al.

enteritidis and the fungus Candida albicans. The

practical part of this work performed in the microbiology

laboratory of Faculty of Sciences in Sarajevo. The

aseptic technique applied during the research.

All experiments performed using sterile dishes and

utensils. Culture medium Mueller-Hinton agar prepared

for the growth of bacterial cultures used in the study,

(Mueller & Hinton, 1941). For the cultivation of

Candida albicans Sabouraud Dextrose agar was used

(Sabouraud, 1892). The juice of fresh domestic and

imported garlic prepared by crushing small pieces of

garlic in a mortar until a thick mass. The content sieved

through sterile gauze in order to obtain the juice. Juice of

the heat treated domestic and imported garlic was

obtained by a number of cleaned pieces of garlic placed

in separate beakers and cooked at 100 °C for 5 minutes.

Once the cooking was completed, such garlic was

shredded in a mortar and sieved through sterile gauze.

The study used method seeding on the agar plate. The

degree of sensitivity of the tested bacteria and fungi

Candida albicans activity of the garlic was determined

by the disk diffusion method (Bauer et al., 1966). The

sterile filter paper discs of 8 mm in diameter used and

soaked in prepared juices.

After completing the procedure, the dishes carefully

closed with the parafilm and left in the incubator

(manufacturer Sutjeska) for 24 hours at 37 °C, after

which the results recorded. After the incubation period,

the zones of inhibition measured in all of the Petri dishes

by using graph paper.


In this research based on the measured zones of

inhibition, it determined that the fresh homemade garlic

manifests the best antimicrobial effect. It had the highest

level of inhibition on the fungus Candida albicans (62

mm), while the lowest level of antimicrobial activity

recorded in Bacillus subtilis (13mm) (Table 1). Heat-

treated domestic garlic demonstrated the weakest

inhibitory effect in MRSA and the fungus Candida

albicans, wherein the zone of inhibition was 0 mm,

while the other type of bacteria, both Gram-positive and

Gram-negative show slightly better anti-microbial

activity (Table 1.). The highest inhibitory effect recorded

for Salmonella enteritidis (11mm).

Table 1. Results of the study on the antimicrobial effects of fresh

and thermally processed domestic and imported garlic

SPECIES

Zone of inhibition (mm)

Fresh

domestic

garlic

Heat

treated

domestic

garlic

Fresh

imported

garlic

Heat

treated

imported

garlic

Staphylococcus

aureus 31 9 9 9

MRSA 29 0 27 0

Bacillus subtilis 13 8 12 9

Escherichia coli 19 8 13 8

Salmonella

enteritidis 15 11 11 9

Candida

albicans 62 0 54 0

The largest zone of inhibition of 54 mm measured for

Candida albicans, while the least antimicrobial activity

demonstrated by the imported fresh garlic against the

Staphylococcus aureus where the zone of inhibition was

9 mm (Table 1). Heat-treated imported garlic shows little

or no antimicrobial effects. On MRSA and Candida

albicans, heat-treated imported garlic showed no

antimicrobial effect while the effects on the other type

of bacteria, indicate a very weak activity (Table 1).

In addition to the bactericidal and fungicidal activity on

selected types of microbes, fresh local and imported A.

sativum showed bacteriostatic activity, or affect their

reproduction that it ends their metabolic activity.

Bacteriostatic effect observed for MRSA with the best

performance shown by the fresh homegrown A. sativum.

Zones of inhibition measured, and the results presented

in Table 2.

Table 2. Bacteriostatic agency of A. sativum on MRSA

Type of Garlic Juice Zone of inhibition (mm)

Fresh Domestic 5

Boiled Domestic 0

Fresh Chinese 4

Boiled Chinese 0

Comparative analysis showed better effects of fresh

garlic in relation to heat treatment. In comparing the

zones of inhibition of fresh domestic and imported fresh,

it is important to emphasize that the strongest

antimicrobial effects showed fresh garlic of domestic

origin. The biggest difference in the measured zones of

inhibition that emerged because of the activity of

domestic and imported Allium sativum L., was observed

on the tested bacteria Staphylococcus aureus, where the

difference between fresh local garlic, which showed the

strongest antimicrobial effects, and imported fresh

garlic, which showed weak antimicrobial effect on the

bacteria was even 22 mm (Table 1). From the research

conducted that the domestic fresh garlic had the best

effects to the type of fungus Candida albicans.

Numerous research has demonstrated that allicin, one of

the active ingredients of fresh crushed garlic exhibities

different antimicrobial activity (Ankri & Mirelman,

1999; Goncagul, 2010; Ross et al., 2001).

Allicin has been shown that in pure form it displays:

antibacterial activity against a broad spectrum of Gram-

positive and Gram-negative bacteria, particularly anti-

fungal activity against Candida albicans, anti-parasitic

activity and antiviral activity (Ankri & Mirelman, 1999).

Allicin and its derivatives inhibit the cysteine protease,

thereby acting antiparasitic on the human and animal

pathogenic protozoa (Waag et al., 2010).

From the research conducted that the domestic fresh

garlic had the best effects to the type of fungus Candida

albicans.

Studies have shown that scanning electron microscope

and the failure of cells that were treated with garlic,

affects the structure and integrity of the outer surface of

fungi cells (Ghannoum, 1988).

Since the cells of Gram-negative bacteria have beside a

peptidoglycan layer also an outer lipid membrane, for a

substance to exhibit any antibacterial activity on these

bacteria, the lipid membrane must at least be partially


dissolved or pores created in it to act on the permeability

of the membrane, leading to molecules and ions from

bacterial cells leaking, and at the end, cracking the

bacteria (Zaika, 1988).

Results of this research correlated with the reference

literature (Gongacul, 2010; Ghannoum, 1988; Lemar,

2002; Shuford, et al., 2005; Zaika, 1988) where in both

domestic and imported Allium sativum L. showed

weaker antimicrobial activity against Gram-negative

bacteria Escherichia coli and Salmonella enteritidis than

the tested Gram - positive bacteria (Staphylococcus

aureus, MRSA and Bacillus subtilis).

CONCLUSIONS

Tested domestic and imported garlic showed

antimicrobial effect against all tested of the Gram-

positive (Staphylococcus aureus, MRSA and Bacillus

subtilis) and Gram-negative bacteria (Escherichia coli

and Salmonella enteritidis), and the fungus Candida

albicans. The investigated fresh domestic and imported

garlic showed bateriostatic activity against the

methicillin-resistant Staphylococcus aureus (MRSA).

Fresh domestic and imported garlic showed better

antimicrobial effect compared to heat treated domestic

and imported garlic. The strongest antimicrobial activity

against all species tested was found in fresh homemade

garlic. Fresh domestic and imported garlic showed the

strongest antimicrobial activity against Candida

albicans. Following these findings, particularly the

intense antimicrobial effect of Allium sativum L. against

Candida albicans, we believe that the preparations based

on A. sativum L. could be introduced into clinical

practice in the treatment of infections caused by C.

albicans.

REFERENCES

Agarwal, K. C. (1996). Therapeutic actions of garlic

constituents. Med. Res. Rev., 16, 111-124.

Ankri, S. & Mirelman D. (1999). Antimicrobial

properties of allicin from garlic. Microbes and

Infection. 1, 125-129.

Bauer, A. W, Kirby, W. M., Sherris, J. C., Turck, M.

(1966). Antibiotic susceptibility testing by a

standardized single disk method. Am J Clin. 45,

493-496.

Ghannoum, M. A. (1988). Studies on the anticandidal

mode of action of Allium sativum (garlic). J.

Gen. Microbiol., 134, 2917-2924.

Goncagul, G. (2010). Antimicrobial effect of garlic

(Allium sativum). Recent Pat Antiinfect Drug

Discov., 5 (1), 91-3.

Lemar, K. M., Turner, M. P. & Lloyd, D. (2002). Garlic

(Allium sativum) as an anti-Candida agent: a

comparison of the efficacy of fresh garlic and

freeze-dried extracts. J. Appl. Microbiol. 93,

398-405.

Mueller, J. H. & Hinton, J. (1941). A protein-free

medium for primary isolation of gonococcus and

meningococcus. Proc. Soc. Exp. Biol. Med. 48, 3330-

333.

Ross, Z. M., O'Gara, E. A., Hill, D. J., Sleightholme, H.

V. & Maslin, D. J. (2001). Antimicrobial

Properties of Garlic Oil against Human Enteric

Bacteria: Evaluation of Methodologies and

Comparisons with Garlic Oil Sulfides and

Garlic Powder. Appl Environ Microbiol., 67 (1),

475–480.

Sabouraud, R. (1892). Ann. Dermatol. Syphilol. 3, 1061.

Shuford, J. A., Steckelberg, J. M. & Patel, R. 2005.

Effects of Fresh Garlic Extract on Candida

albicans Biofilms. Antimicrob. Agents

Chemother., 1, 473.

Waag, T., Gelhaus, C., Rath, J., Stich, A., Leippe, M.,

Schirmeister, T. (2010). Allicin and derivaates

are cysteine protease inhibitors with

antiparasitic activitiy. Bioorg Med Chem Lett.,

20, 5541-5543.

Zaika, L. L. (1988). Spices and herbs: their antibacterial

activity and its determination. J Food Saf. 23, 97-118.

20 Strika et al.

Summary/Sažetak

Bijeli luk već hiljadama godina se koristi kao hrana i lijek. Budući da bijeli luk sadrži različite biološki aktivne

materije i pri tome djeluje kao antibiotik i fungicid, svrha ovog istraživanja je ispitati osjetljivost tri vrste Gram-

pozitivnih bakterija: Staphylococcus aureus, meticilin-rezistentni Staphylococcus aureus (MRSA) i Bacillus

subtilis; dvije vrste Gram-negativnih bakterija: Escherichia coli i Salmonella enteritidis, te gljivicu Candida

albicans. Stepen osjetljivosti ispitivanih mikroorganizama na djelovanje svježeg i termički obrađenog domaćeg

i uvoznog bijelog luka određivan je disk difuzionom metodom. Testirana antimikrobna-test supstanca pokazala

je antimikrobni efekat na sve ispitivane vrste Gram-pozitivnih i Gram-negativnih bakterija kao i fungicidno

djelovanje na gljivicu Candida albicans. Najjače antimikrobno djelovanje na sve testirane vrste pokazao je

svježi domaći bijeli luk. Preparati na bazi A. sativum mogli bi se uvesti u kliničku praksu kod liječenja infekcija

uzrokovanih C. albicans.

Heavy metals pollution in children playgrounds -an environmental

modelling and statistical analysis

Šapčanin A1,2

*, Čakal M2, Ramić E

1, Smajović A

1, Pehlić E

3

1Faculty of Pharmacy, University of Sarajevo, Zmaja od Bosne 8, 71 000 Sarajevo, Bosnia and Herzegovina 2Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovo šetalište 8, 71 000 Sarajevo, B&H

3Faculty of Biotechnical Sciences, University of Bihac, Luke Marjanovića bb, 77 000 Bihac, Bosnia and Herzegovina

INTRODUCTION

Mathematical and computer modelling help us with

understanding processes occurring in soils. A number of

models are being developed now which can quantitatively

predict movements and sorption of heavy metals in soil

with good accuracy (Dube et al., 2001). In recent years,

researchers have focused on modelling of

multicomponent reactive transport and have developed

models to study the mobility of potentially toxic heavy

metals in the surface soils. The heavy metals are reactive

and undergo various chemical transformations in the

contaminated soil. Numerous studies have shown that the

major sources of heavy metal pollution in urban soils

include emissions from traffic (exhaust, tire wear debris

particles, particles formed by weathering street),

industrial wastes (from power plants, coal combustion,

metallurgical industry, automobile repair plants, chemical

plants), household garbage, building and weathered

particles of sidewalk and precipitation in the atmosphere,

etc (Hu et al., 2013; Su et al.. 2014). The content of heavy

metals in soils can vary widely, even in uncontaminated

soils (Qayyum et al., 2015). All inorganic pollutants are

considered to be the most common and potentially

harmful substances in urban soil in areas affected by

anthropogenic pollution (EC, 2013). Due to the fact that

children, the most vulnerable population, are in direct

contact with surface soils, it has become clear that soils

from children playgrounds should be examined with

special care and attention. Polluted soils can directly

affect children’s health as pollutants and particles from

playgrounds can enter their bodies through oral ingestion,

inhalation, or in a dermal contact. That is why soil

samples, and the soils generally, affect human health,

have become a very good diagnostic tool for an

assessment of the environmental status of the city

(Sapcanin et al., 2016). In urban environments such as

children playgrounds, polluted soils can have a direct

influence on infants and children’s health, because of

heavy metals inherent toxicity.

Heavy metals once introduced to the environment by one

particular method may spread to various environmental

components, which may be caused by the nature of

interactions occurring in this natural system. Heavy

metals may chemically or physically interact with the

natural compounds, which changes their forms of

existence in the environment. In general, they may react

with particular species, change oxidation states and

precipitate (Dube et al., 2001.) Heavy metals are

ecologically very important, they are not biodegradable

and they move through the ecosystem.

Models could be used to simulate target variable

responses to changes in very complex systems such as

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Keywords: Soil Pollution

Children Playgrounds

Heavy Metals Modelling

Correlation Coefficient.

*Corresponding author: E-mail:[email protected] Phone: 00-387-33-586187

Abstract: Models could be used to simulate target variable responses to changes in very

complex systems such as soils polluted by heavy metals. Chemical properties of soil such as

pH in H2O, pH in 1 mol/L KCl, humus and CaCO3 could influence metal mobility and can

be used to assess impact of various antropogenic activities. The soil samples were collected

from playgrounds located in different areas of Sarajevo. Heavy metals: Cd, Pb, Cu and Zn

and basic chemical properties of soil were determined. Statistical analysis was conducted to

obtain the correlation coefficient of two selected variables in a data sample, as a normalized

measurement of how the two are linearly related. Determined content (mg/kg) for Cd, Pb,

Cu and Zn in the spring season were in the ranges of: 0.91-2.15; 26.69-118.97; 19.14-80.21;

75.85-161.45 and in the autumn season were in the ranges of: 0.80-2.14; 41.07-152.71;

29.46-140.74; 71.77-199.04, respectively. The results showed that the highest correlation

coefficient was 0.91, for the total content of Cu in the soils in regard to the content of Pb in

the autumn season and this indicates a strong and positive correlation. Generally, our results

could be used for prediction of heavy metal distribution in playground soil.

22 Šapčanin et al.

soils polluted by heavy metals. Determination of

chemical properties of soil such as pH in H2O, pH in 1M

KCl, humus and CaCO3 could influence metal mobility

and can be used to assess impact of various antropogenic

activities in soils of interest and to give the

environmental buffering capacity and to model pathways

in the polluted soils leading to the human health risk of

exposure (Kabata-Pendis and Mukherjee, 2007). Every

model is created by using the data received as an

experimental result from natural or simplified (pseudo-

natural) system, which must be accurately defined and in

controlled conditions. Obtained results are transformed

into a general formula. On the basis of this formula, the

scientist chooses a suitable model (Altman, 1991;

Armitage and Berry, 1994). The last step is checking the

model in different physiochemical conditions to define

where and when this model can be applied. There are two

types of approach to modelling the system, the predictive

and the descriptive ones. In the predictive modelling, the

primary objective is to obtain the best possible fit to the

available data, since in that way there will be the

maximum possible confidence in the predictions made by

the model in regions where there are no experimental data

with which to compare it. In the descriptive modelling, on

the other hand, the goal is to use the model to gain more

information about the way in which the real system

functions. Furthermore, the primary intent is not to obtain

the best possible fit to experimental data, but rather to

increase knowledge of the binding precess. In any case,

one of the first stages in the modelling process is the

correlational and regressional analysis. By using the

regression and correlation, the correlation between two or

more variables is analyzed. The correlation implies the

analysis of the strength and direction of the correlation,

while the regression implies the analysis of the shape and

direction of the correlation, and the analysis in terms of

independent/dependent (predictor/outcome) variables,

with the intent of making prediction. (Altman, 1991;

Armitage and Berry, 1994; Bland, 2005). In the

regressional model, knowledge of values of independent

variables enables predicting values of the dependent

variables.

Whenever there is a significant correlation between the

two variables, the value of one variable can be used for

predicting the value of the other variable. (Nedunuri et

al., 1995).

MATERIAL AND METHODS

For the purpose of this study, the soil samples were

collected from selected public children playgrounds

located in eight different urban areas of Sarajevo

according to ISO 11047 protocol, in the spring and

autumn seasons.

Ten sub-samples were collected from the top 10 cm layer

of the soil and mixed to obtain a bulk composite sample.

Samples were collected with a stainless trowel and

transferred to the laboratory in plastic bags. Stones and

foreign objects were hand-removed, and the samples were

air-dried for seven days.

Furthermore, the samples were then ground in order to

obtain a fine homogenous powder. Basic properties ( pH

in H2O, pH in 1M KCl, humus and CaCO3) of the

examined soil samples were determined by standard

methods of soil analysis (Sapcanin et al., 2016).

Samples for the determination of heavy metals Cd, Pb,

Cu, and Zn were prepared by microwave – assisted acid

digestion and determined by using an atomic absorption

spectrophotometer, Shimadzu AA 6800, according to

Environmental Protection Agency (EPA) protocols.

Mathematical-statistical analysis

Statistical analysis was conducted to obtain

the correlation coefficient of two selected variables in a

data sample, as a normalized measurement of how the

two are linearly related. Estimation of dependence of the

selected variables has been carried out according to the

following criteria:

if the value of the correlation coefficient is ≥ 0.70, then

correlation is strong;

if the value of the correlation coefficient between 0.30 –

0.69, then correlation is medium;

if the value of the correlation coefficient is < 0.30, then

correlation is weak,

And if the value of the correlation coefficient is 0.0, then

there is no lienar correlation (which does not exclude the

possibility of having a non-linear form of correlation).


The results of the determination of some basic properties

of the examined samples from the public park and

playground soils in the spring and autumn seasons are

presented in Table 1.

Results of the pH values for the active acidity in the soil

in the selected public children playground soils range

from: 7.94 to 8.29, so that the examined soils are

classified as alkaline ones. Results of the pH values for

the substituted acidity in the soil on the selected public

children playground soils range from: 7.26 to 7.68, so that

the examined soils are classified as alkaline ones – in

alkaline soils sorption of metals is lower, however, metals

remain for longer periods in the soils. The content of

humus in the soil in the selected public children

playground soils ranges from: 2.94-11.75, and based on

the humus content, the examined soils are classified as

soils with weak or strong presence of humus.


Table 1. Basic chemical properties (pH in H2O, pH in 1 mol dm−3

KCl, humus, and CaCO3) of the soil samples from

selected playgrounds in the spring and autumn seasons.

The content of CaCO3 in the soil in the selected public

children playground soils ranges from: 1.49-36.31, and

based on the content of CaCO3 in the soil, the examined

soils are classified as soils with weak or strong carbon

content.

Table 2 shows basic statistical parameters: mean value,

standard deviation, median, variance, minimum and

maximum value and the range of the content (mg/kg) of

metals/metalloids, in the soil collected from the old

children playground in the spring season in Sarajevo.

Table 2. Basic statistical parameters calculated for the content of metal (mg/kg) in public children playground areas in

Sarajevo in the spring season

Results show that maximum values for the content of

metals Cd, Pb, Cu i Zn are higher from the target value

according to the Dutch legislation. Obtained maximum

values have not exceeded the values which may require

intervention in form of remediation, nevertheless, the

obtained values do not exclude risks which may be

posed to the ecosystem.

According to the results obtained for maximum values of

the content of metals/metalloids, the order is as follows:

Zn > Pb > Cu > Cd.

Table 3 shows basic statistical parameters: mean value,

standard deviation, median, variance, minimum and

maximum value and the range of the content (mg/kg) of

metals/metalloids, in the soil collected from the old

children playground in the autumn season in Sarajevo.

Table 3. Basic statistical parameters calculated for the content of metal (mg/kg) in public children playground areas in

Sarajevo in the autumn season

Playground pH in H2O pH in 1M KCl-u Humus (%) CaCO3 (%)

Spring Autumn Sprin

g

Autumn Spring Autumn Spring Autumn

1 8.17 8.16 7.59 7.30 5.83 5.63 14.26 18.28

2 8.26 8.17 7.73 7.58 3.38 5.54 15.88 17.95

3 8.34 8.14 7.74 7.68 2.94 4.14 17.63 20.73

4 8.14 7.97 7.57 7.54 4.48 4.66 25.20 27.23

5 8.20 8.22 7.63 7.63 11.75 6.48 17.88 36.31

6 8.12 8.04 7.63 7.51 5.16 5.16 5.90 17.15

7 8.29 8.06 7.26 7.61 6.02 6.23 19.73 1.49

8 8.13 7.94 7.41 7.46 2.97 3.85 5.49 4.57

Heavy

metal

Mean

(mg/kg) SD Median Variance Min Max Range

Cd 1.42 0.56 1.49 0.32 0.48 2.1596 1.68

Pb 68.65 32.15 65.21 1033.71 26.7 118.9793 92.28

Cu 45.36 21.94 40.5 481.46 19.14 80.2156 61.07

Zn 129.94 35.99 137.66 1295.24 75.85 173.44 97.58

Heavy

metal

Mean

(mg/kg) SD Median Variance Min Max Range

Cd 1.56 0.47 1.64 0.23 0.81 2.14 1.34

Pb 79.13 46.30 55.33 2144.12 41.07 152.72 111.65

Cu 54.54 39.72 40.73 1577.69 18.92 140.74 121.81

Zn 125.94 40.95 123.75 1677.18 71.76 199.04 127.27

Determined content (mg/kg) for Cd, Pb, Cu and Zn in

the spring season were in the ranges of: 0.91-2.15;

26.69-118.97; 19.14-80.21; 75.85-161.45 and in the

autumn season were in the ranges of: 0.80 - 2.14; 41.07-

152.71; 29.46-140.74; 71.77-199.04, respectively. The

results showed that the soils of public parks and

playgrounds could be marked as slightly to medium

contaminated by determined heavy metals.

Results show that maximum values for the content of

metals Cd, Pb, Cu i Zn are higher from the target value

according to the Dutch legislation (VROM, 2000).

Obtained maximum values have not exceeded the values

which may require intervention in form of remediation,

nevertheless, the obtained values do not exclude risks

which may be posed to the ecosystem.

According to the results obtained for maximum values of

the content of metals/metalloids, seasonal variations

have been noticed, ie. maximum values of the content of

Pb and Cr were higher in autumn in respect to spring,

while maximum values of the content of Ni, Cu, Zn, and

Se, were higher in spring in respect to autumn. Based on

the results obtained for maximum values of the content

of metals/metalloids, the order is as follows: Zn > Pb >

Cu > Cd.

Results of the correlation of the total content of Cd in

relation to the content of humus in the spring season are

shown in Graph 1.

Graph 1. The total content of Cd in the soil in relation to the

content of humus in the spring season

The results show that the highest correlation coefficient

for the total content of Cd in the soils in relation to the

content of humus in the spring season was 0.78, which

indicates to a strong and positive correlation. That

means that the increase of the content of humus affects

the increase of the total content of Cd.

Results of the correlation of the total content of Pb in


shown in Graph 2.

Graph 2. The total content of Pb in the soil in relation to the


The results show that there is a correlation between the

total content of Pb in the soil and the content of humus

as the value of the correlation coefficient is 0,47, which

indicates to a medium and positive correlation.

Results of the correlation of the total content of Cu in


shown in Graph 3.

Graph 3. The total content of Cu in the soil in relation to the


The results show that the correlation coefficient for the

total content of Cu in relation to the content of humus is

0,38, which indicates to a medium and positive

correlation of these two parametres.

Results of the correlation of the total content of Zn in


shown in Graph 4.

Graph 4. The total content of Zn in the soil in relation to the



total content of Zn in relation to the content of humus is



Results of the correlation of the total content of Cd in

relation to the content of CaCO3 in the spring season are

shown in Graph 5.


Graph 5. The total content of Cd in the soil in relation to the

content of CaCO3 in the spring season


total content of Cd in relation to the content of CaCO3 is



Results of the correlation of the total content of Zn in

relation to the content of CaCO3 in the spring season are

shown in Graph 6.

Graph 6. The total content of Zn in the soil in relation to the

content of CaCO3 in the spring season


total content of Zn in relation to the content of CaCO3 is



Basic chemical properties of soil such as pH in H2O, pH

in 1 mol dm−3

KCl, humus, and CaCO3 could influence

metal mobility and the determined values could be used

to model pathways in the polluted soils.

The correlations between the heavy metals studied are

shown in the Graphs 7 to 11, as follows:


content of Cu in the spring season


content of Zn in the spring season

Graph 9. The total content of Cu in the soil in relation to content

of Zn in the spring season


content of Cu in the autumn season


content of Zn in the autumn season

Results of the correlation between the heavy metals

studied offer remarkable information on the sources and

pathways of the heavy metals. Pb was strongly

correlated with Cu (r =0,87) and Zn (r =0,8) in the spring

season. Cu was strongly correlated with Zn (r =0,87) in

the spring season. Pb was strongly correlated with Cu

(r =0,91) and Zn ( r=0,78) in the autumn season.

The highly significant positive correlation between the

heavy metals investigated indicates that their compounds

occurred from various anthropogenic activities in the

children playgrounds environment.

CONCLUSIONS

26 Šapčanin et al.

The presence of heavy metals in soils represents a

significant environmental hazard, and one of the most

difficult contamination problems to solve. There are two

main reasons: firstly, due to the chemical character of

heavy metals - they are not subjected to biodegradation

processes, and they accumulate in the environment; and

secondly, due to the complexity of the soil matrix. The

unhomogeneity of soils is so high, that we cannot

provide all the features of soil samples without

employment of some approximations. Simplification of

this matrix increases chances of recognition of basic soil

processes. Another option which could enable us to

understand better processes occurring in the soils is to

employ a computer simulation. However, it seems most

effective to apply computer methods with the simulation

of natural physicochemical processes in a simplified soil

matrix. It can be concluded that, generally speaking, the

results obtained from this research can be used for

predicting the distribution of content of heavy metals in

the soil in the children playground areas.

REFERENCES

Altman , D.G. (1991). Practical Statistics for Medical

Research. London. Chapman & Hall.

Armitage, P., Berr, P.. (1994). Statistical Methods in

Medical Research. Oxford, Blackwell Science Ltd.

Bland, M. (2005). An Introduction to Medical Statistics

( 3rd

Ed). Oxford, Oxford University Press.

Dube, A., Zbytniewski, R., Kowalkowski, T.,

Cukrowska, E., Buszewski, B., (2001). Adsorption

and Migration of Heavy Metals in Soil. Polish

Journal of Environmental Studies, 10(1), 1-10.

Hu, Y., Liu, X., Bai, J., Shih, K., Zeng, E.Y., Cheng, H.

(2013). Assessing heavy metal pollution in the

surface soils of a region that had undergone three

decades of intense industralization and urbanization.

Environ Sci Pollut Res, 20, 6150–6159.

Kabata-Pendis, A., Mukherjee, A.B. (2007). Trace

Elements from Soil to Human. Berlin:, Springer

Verlag, p.p. 9-38.

Rogozan, G.C., Micle, V., Coste (Bina), A. (2013).

Mathematical Models of Heavy Metals Mobility in

Soils. ProEnvironment, 6, 450 – 458.

Qayyum, S., Khan, I., Meng, K., Zhao, Y., Peng, C.

(2015). Remediation technologies for heavy metals

contaminated soil. Journal of international academic

research for multidisciplinary, 3(11), 96-105.

Schnoor, L.J., (1996). Modeling trace metals. In:

Schnoor L.J. Ed. Environmental modeling, Fate and

transport of pollutants in water, air and soils. United

States of America, Yohn Wiley & Sons, Inc. p.p.

381.

EC (2013). Science for Environment Policy In-depth

Report: Soil Contamination: Impacts on Human

Health. Report produced for the European

Commission DG Environment, September 2013.

Science Communication Unit, University of the West

of England, Bristol .Available at:

http://ec.europa.eu/science-environment-policy

Nedunuri, K..V., Govindaraju, R.S., Erickson, L.E.,

Schwab, A.P. (1995). Modeling of heavy metal

movement in vegetated, unsaturated soils with

emphasis on geochemistry. In the Proceedings of the

10th Annual Conference on Hazardous Waste

Research , p.p. 57-66.

Sapcanin, A., Cakal, M., Jacimovic, Z., Pehlic, E.,

Jancan, G. (2016). Soil Pollution fingerprints of

children playgrounds in Sarajevo city, Bosnia and

Herzegovina. Environmental Science and Pollution

Research, doi:10.1007/s11356-016-6301-5

Su, C., Jiang, L., Zhang , W. (2014). A review on heavy

metal contamination in the soil worldwide: situation,

impact and remediation techniques. Environmental

Skeptics and Critics, 3(2), 24–38.

VROM (2000) Circular on target values and intervention

values for soil remediation. Ministry of Housing,

Spatial Planing and Environment, VROM, http://

www.minvrom.nl/minvrom/docs/bodem/S&I2000.pd

f

Summary/Sažetak

Modeli se mogu koristiti za simulaciju odgovora ciljnih varijabli na promjene u veoma kompleksnim sistemima kao što su

zemljišta kontaminirana teškim metalima. Karakteristike zemljišta kao što su pH u void, pH u KCl-u, humus i CaCO3

mogle bi utjecati na mobilnost metala i mogu biti upotrebljena za procjenu djelovanja raznolikih antropogenih aktivnosti.

Uzorci tla prikupljeni su sa igrališta lociranih u različitim dijelovima Sarajeva. Teški metali: Cd, Pb, Cu i Zn kao i osnovne

hemijske karakteristike tla su određeni. Statistička analiza je izvedena da se dobiju podaci o korelacijskim koeficijentima

za dvije odabrane varijable u uzorku podataka kao normaliziranog mjerenja da li su varijable linearno povezane. Izmjereni

sadržaj (mg/kg) za Cd, Pb, Cu i Zn u proljetnjoj sezoni kretali su se u rasponu od: 0.91-2.15; 26.69-118.97; 19.14-80.21;

75.85-161.45 i u jesenjoj sezoni kretali su se u rasponu od: 0.80-2.14; 41.07-152.71; 29.46-140.74; 71.77-199.04,

respektivno. Rezultati pokazuju da je najveća vrijednost korelacijskog koeficijenta iznosila 0.91, za ukupni sadržaj Cu u

http://www.minvrom.nl/minvrom/docs/bodem/S&I2000.pdf

http://www.minvrom.nl/minvrom/docs/bodem/S&I2000.pdf


tlu, u odnosu na sadržaj Pb u jesenjoj sezoni i to indicira jaku i pozitivnu korelaciju. Generalno bi se naši rezultati mogli

upotrijebiti za predviđanje raspodjele teških metala u tlu igrališta.

Synthesis and Characterization of Neutral Ru(III) Complexes

Containing Schiff Bases and N-donor Heterocyclic Ligands

Begić S.*, Ljubijankić N.

Department of Chemistry, Faculty of Science, University of Sarajevo, Sarajevo 71 000, Bosnia and Herzegovina

INTRODUCTION The interest in the synthesis and characterization of Ru(III) complexes containing a Schiff base is due to their many significant biological activities, especially anticancer (Ejidike et al, 2016) or antibacterial (Kahrovic, Bektas, et al., 2014; Priya et al, 2009) activities. Schiff bases derived from the condensation of 5-X-salicyladehyde (where X = Cl or Br) and aniline represent an important class of chelating ligands. There are relatively small number of complexes containing these Schiff bases whose structures are stored in Cambridge Structural Database (Blagus et al, 2010). Ruthenium(III) complexes with Schiff bases derived from 5-substituted-salicylaldehyde are described as DNA intercalators (Ljubijankic et al, 2013; Kahrovic et al., 2014; Begic-Hairlahovic et al, 2014) and electrochemical mediators (Turkusic and Kahrovic, 2012, Kahrovic et al., 2012, Kahrovic and Turkusic, 2012). Pyridine, pyrimidine and their derivatives have been widely used in medicinal applications (Akalin and Akyuz, 2008; Quin and Tyrell, 2010). The pyridine and pyrimidine ring

systems provides a potential binding site for metals. Complex compounds containing pyridine or pyrimidine ring systems possesses a broad range of biological activities and can be used as potentially active therapeutic compounds (Colluccia, Natile, 2007; Kostova, 2006). In this study, we report the synthesis and spectral characterization of Ru(III) neutral complex compounds with N-phenyl-5-X-salicylideneimine (where X = Cl or Br) and N-donor heterocyclic ligands, pyridine (py) or pyrimidine (pym). The present work is a continuation of our previously reported study on the synthesis, characterization and interaction with CT DNA of Ru(III) complexes with indazole and Schiff base derived from 5-Chlorosalicylaldehyde (Begic-Hairlahovic et al, 2014). The Schiff base and N-donor heterocyclic ligands used in this study are shown in (Fig. 1).

OH

X C=N

H

N

N

N

Figure 1: Structure of the Schiff bases (where X = Cl or Br), pyridine and pyrimidine.




YEAR

Issue

27-32 UDC: __________________________


Article info Received: 04/12/2016 Accepted: 22/12/2016 Keywords: Ru(III) neutral complex Schiff base pyridine pyrimidine

*Corresponding author: E-mail: [email protected] Phone: 00-387-33-279950 Fax: 00-387-33-649359

Abstract: Two new neutral complex compounds of Ru(III) with Schiff bases and N-donor

heterocyclic ligands have been synthesized. Based on MALDI TOF/TOF mass spectra,

CHN elemental analysis, infrared and electronic spectra the synthesized compounds

were formulated as [RuCl(N-Ph-5-X-salim)2B], where X = Cl for B = Py and X = Br

for B = Pym. In the octahedral environment of Ru(III), bidentate Schiff bases acts as

anionic ligand where coordination occurs via deprotonated phenolic oxygen and

azomethine nitrogen atom. Coordination of monodentate N-donor heterocyclic ligands

occurs through free electronic pair on the nitrogen atom.


28 Begić et al.

EXPERIMENTAL Materials All chemicals were of analytical grade and were used as received without any purification with exception of aniline which is purified by distillation.

Starting materials Schiff bases, N-phenyl-5-X-salicylideneimine (where X = Cl or Br) were synthesized according to the literature procedures (Dholakiya and Patel, 2002) and freshly prepared solutions were used for the synthesis of starting materials. Sodium dichloro-bis-[N-phenyl-5-X-salicylideneiminato-N,O]ruthenate(III), hereinafter Na[RuCl2(N-Ph-5-X-salim)2] where X = Cl or Br were prepared using the reported procedures (Ljubijankic et al., 2013) and were used without any purification as starting Ru(III) compounds in the synthesis of new neutral complexes. The purity of starting Ru(III) compounds were checked by infrared spectroscopy. Synthesis of Complex 1 The preparation of Chloro(pyridine)bis[N-phenyl-5-chlorosalicylideneiminato-O,N]ruthenium(III), [RuCl(N-Ph-5-Cl-salim)2py], hereinafter Complex 1 was carried out by refluxing an ethanolic solutions of Na[RuCl2(N-Ph-5-Cl-salim)2] and an excess of pyridine. Absolute ethanol was added to 60 ml of ethanolic solution of Na[RuCl2(N-Ph-5-Cl-salim)2] (41.04 mg; 0.062 mmol) and pyridine (10 µL; 0.124 mmol). The mixture was refluxed for 10.5 hours at temperature 75 °C whereby the solution changed color from dark green to violet blue. The resulting solution was filtered off and kept in an ice-salt bath for seven days. The dark blue product was washed with water and diethyl ether to remove sodium chloride and excess of pyridine. The synthesized complex was dried under vacuum in a desiccator. Yield: 68 %. Complex 1: Anal. calcd for C31H23Cl3N3O2Ru: C 55.00, H 3.42, N 6.21. Found: C 53.68, H 3.79, N 6.37. MALDI TOF/TOF MS (m/z) calcd for [C31H23Cl3N3O2Ru], 676.9883; found, 676.9889; IR (KBr, cm-1) 1589 s [ν(ring mode)], 1004 m [(ring breathing mode)], 669 vw [ν(Ru–N), 438 wv [ν(Ru–O)]; UV-Vis (CH2Cl2, λ/nm) 245, 260, 350, 607. Synthesis of Complex 2

The compound, Chloro(pyrimidine)bis[N-phenyl-5-bromosalicylideneiminato-O,N]ruthenium(III), [RuCl(N-Ph-5-Br-salim)2Pym], hereinafter Complex 2 was prepared by stirring an ethanolic solutions of Na[RuCl2(N-Ph-5-Br-salim)2] and an excess of pyrimidine. The solution of pyrimidine (5 µL; 0.06 mmol) in 2 mL absolute ethanol was added to the solution of Na[RuCl2(N-Ph-5-Br-salim)2] (25.1 mg; 0.034 mmol) in absolute ethanol (40 mL). The mixture was stirred at room temperature for 29 hours until a light green solution

was obtained. The resulting solution was evaporated to dryness and residue washed several times with diethylether and water to remove excess of pyrimidine and sodium chloride. The green product was dried in a vacuum desiccator. Yield: 50 %. Complex 2: Anal. calcd for C30H22Br2ClN4O2Ru: C 46.99, H 2.89, N 7.31. Found: C 45.04, H 2.94, N 7.02. MALDI TOF/TOF MS (m/z) calcd for C30H22Br2ClN4O2Ru, 766.8823; found, 766.8804. IR (KBr, cm-1) 1589 s [ν(ring mode)], 1002 m [(ring breathing mode)], 663 wv [ν(Ru–N), 434wv [ν(Ru–O)]. UV-Vis (CH2Cl2, λ/nm) 245, 260, 350, 607. Instrumentation – Physical measurements

Synthesized compounds were characterized by MALDI TOF/TOF mass spectrometry, CHN analysis, cyclic voltammetry, IR and UV/Vis spectrometry. The mass spectra were recorded on a MALDI TOF/TOF (matrix-assisted laser desorption / ionization-time-of-flight) Analyzer model 4800 Plus (Applied Biosystems Inc., Foster City, CA, USA). Small amount of sample was mixed with 10 µL of MALDI matrix (DHAP (2,6-dihydroxyacetophenone); 5 mg/mL) and 1 µL was spotted on MALDI plate. The mass spectra were acquired in m/z range from 10 to 1000 Da (focus mass 500 Da, delay time 300 ns). Thiamine mononitrate, azithromycin and angiotensin were used as internal standards to calibrate the instrument. The analysis of carbon, hydrogen and nitrogen was carried out by a Perkin Elmer 2400 Series CHNS/O Analyzer. Infrared spectra (4000 – 400 cm-1) were collected on a BX FTIR Perkin Elmer Spectrum System with samples prepared as KBr pellets. The electronic spectra were obtained on a Perkin Elmer UV/Vis spectrometer model Lambda 35. The characterization of Ru(III) complexes by cyclic voltammetry (CV) was investigated using a potentiostat/galvanostat Autolab PGSTAT 12 Analyzer equipped with glassy carbon (GC), Ag/AgCl and Pt wire as working, reference and auxiliary electrode, respectively. The cyclic voltammograms of the complexes were recorded in acetonitrile (MeCN) at scan rate 0.2 V s-1 where tetraethylammonium bromide (Et4NBr) was used as supporting electrolyte.

RESULTS AND DISCUSSION The starting Ru(III) compounds, Na[RuCl2(N-Ph-5-X-salim)2] (where X = Cl or Br) were synthesized by reacting RuCl3 with freshly prepared Schiff bases, 5-X-salicylideneimine (Dholakiya and Patel, 2002) in 1 : 2 molar ratio in absolute ethanol. The freshly prepared Na[RuCl2(N-Ph-5-X-salim)2] compounds were used for the synthesis of new complex compounds. Neutral ruthenium(III) complex compounds of general formula [RuCl(N-Ph-5-X-salim)2B] (where X = Cl for B = py or X = Br for B = pym) were prepared by reacting Na[RuCl2(N-Ph-5-X-salim)2]with an excess of py or pym in absolute ethanol as shown in (Fig. 2). The synthesis of

Bulletin of the Chemists and Technologists of Bosnia and Herzegovina Year, Issue, 27-32 29

the complexes was carried out in relative mild conditions by replacement of easily outgoing chloride ion in Na[RuCl2(N-Ph-5-X-salim)2] with py or pym. The products are stable in air, insoluble in water, soluble in dimethyl sulfoxide (DMSO), dichloromethane, acetonitrile (MeCN), dimethylformamide (DMF),

tetrahydrofuran (THF). Based on mass spectra, CHN elemental analysis, infrared and electronic spectra the synthesized compounds were formulated as [RuCl(N-Ph-5-X-salim)2B], where X = Cl for B = py and X = Br for B = pym.

Cl

N CH

X

ORu

ClO

NCH

X

- Na+

N CH

X

ORu

O

NCH

X

Cl+ B

B

+ NaClaps. EtOH

B = py; reflux, 75 oC, 10.5 hB = pym; magn.stirr., room temp., 29 h

Figure 2: Formation of new Ru(III) complexes. (Where X = Cl for B = py and X = Br for B = pym).

MALDI-TOF/TOF mass spectrometry results confirmed existence of neutral molecules with general formula [RuCl(N-Ph-5-X-salim)2B] (Table 1).

Table 1: MALDI TOF/TOF results of synthesized compounds

Compound Empirical formula Calc. mass

m/z (100 %)

Measured mass m/z (100 %)

Mass error / ppm

Complex 1 C31H23Cl3N3O2Ru 676.9883 676.9889 0.88 Complex 2 C30H22Br2ClN4O2Ru 766.8823 766.8804 2.48

The compounds were characterized by IR spectroscopy in the solid state (KBr pellets). The main characteristic vibrations are given in Table 2. In studying coordination effects of metal complexes with pyridine or pyrimidine, ring breathing mode can be used as a guide which shifts to higher wavenumbers upon coordination (Bayari et al, 2003). Also when pyridine or pyrimidine ring nitrogen is involved in complex formation certain vibrational modes increase in value due to both coupling with M-N (pyridine or pyrimidine) vibration and alterations of the force field (Akalin, Akyuz, 2008; Bayari et al, 2003). The strong bands at 992 and 991 cm-1 could be attributed to the ring breathing mode of free pyridine or pyrimidine molecule, respectively. After coordination this bands were shifted for 12 or 11 cm-1 to higher wavenumbers.

Coordination of pyridine or pyrimidine to Ru(III) via electronic pair on the atom nitrogen affects ring stretching vibrations. This frequencies were shifted towards higher wavenumber, which is indicative of coordination of pyridine or pyrimidine through nitrogen atom to the Ru(III). The new weak bands in Complex 1 at 669 and 438 cm-1 could be assigned to Ru-N and Ru-O, respectively. This bands in Complex 2 appears at 663 and 434 cm-1. Characteristic IR vibrations of starting Ru(III) compounds are azomethine C=N, C-O phenolic, Ru-N and Ru-O. After coordination of N-heterocyclic donor ligands this bands are not affected. Infrared spectra of synthesized complexes demonstrate that Schiff bases acts as anionic bidentate ligands while N-heterocycle as monodentate ligand.

30 Begić et al.

Figure 3: FT- IR spectra of Complex 1and Complex 2

Table 2: Characteristic vibrations (cm-1) in FT-IR spectra of starting Ru(III) compounds, neutral complexes, free pyridine and

pyrimidine Complex

1 Complex

2 Py Pym

ν(ring) py, pym

ring breathing py, pym 1589 1004

1589 1002

1580 992

1570 991

ν(C=N)SB 1603 (1602)

1600 (1601)

- -

ν(C=OPh)SB 1295 (1295)

1291 (1289)

- -

ν(Ru–N) 669 (668)

663 (663)

- -

ν(Ru–O) 438 (436)

434 (433)

- -

SB – assigned vibrations in Schiff base;Py, Pym – assigned vibrations in pyridine or pyrimidine; values in parentheses

refer to starting Ru(III) compound

UV/Vis spectra of starting Ru(III) compounds, synthesized compounds and N-donor ligands were recorded in dichloromethane. Electronic spectra exhibit a few characteristic absorptions which are presented in Table 3. UV/Vis spectra of pyridine has one absorption band which could be attributed to π→π* transition, while pyrimidine showed two bands attributed to π→π* and n→π* transitions. In the electronic spectra of starting Ru(III) compounds weak broad absorptions bands centered at 609 nm for X = Cl or 625 nm for X = Br can be attributed to 2T2g→2A2g. In the spectra of neutral complexes this transitions moves towards lower value of wavelength (565 nm for B = py and 609 nm for B = pym). N-donor ligands in the synthesized complexes splits stronger crystal field than chloride ion in the starting material results in higher separation energies of

d-atomic orbitals and moves d-d transitions to higher energies. Since the synthesized complexes are insoluble in water, cyclicvoltammograms were recorded in non-aqueous solvent. The complete scan in the range -1.0 – 0.0 V of Complex 1 and Complex 2 in MeCN/Et4NBr system showed one cathodic wave (-0.981 V and -0.931 V, respectively) and one anodic wave (-0.740 V and -0.738 V, respectively) (Fig. 4). This quasi-reversible waves can be assigned to the couple Ru(III)/Ru(II).

Table 3: Characteristic absorptions in electronic spectra of Na[RuCl2(N-Ph-5-X-salim)2], Complex 1, Complex 2 and

nitrogenous bases in dichloromethane Compound π→π∗ n→π∗

IL

(SB)

d-d

Na[RuCl2(N-Ph-5-Cl-salim)2]

244 sh 348 613

Py 253 - - - Complex 1 244 sh 348 565 Na[RuCl2(N-Ph-5-Br-salim)2]

244 sh 351 625

Complex 2 244 sh 350 611 Pym 243 287 - -

π→π∗ - electronic transition of delocalized electrons of the aromatic system; n→π∗ - electronic transitions of the atoms of

azomethine group or free electron pair on the N atom of pyridine or pyrimidine with aromatic π electrons; IL (SB) – intraligand transition of whole molecule of Schiff base; d-d – transition of

low spin complex; sh-shoulder.

In Table 4 are given characteristic half-wave potentials and peak-to-peak separation values of Complex 1 and Complex 2.

Bulletin of the Chemists and Technologists of Bosnia and Herzegovina Year, Issue, 27-32 31

Figure 4: Cyclic voltammograms of Complex 1 (1) andComplex 2 (2) in MeCN; supporting electrolyte: Et4NBr; potential range: -1.1 – -0.5 V; scan rate 0.2 V/s

Table 4: Characteristic potentials of Complex 1 and Complex 2 from cyclic voltammetric measurements in MeCN / Et4NBr system

Complex 1 Complex 2 Epc/ V -0.981 -0.931 Epa/ V -0.740 -0.738 E1/2 / V -0.861 -0.835 ∆E / V 0.241 0.193

All data are given vs Ag/AgCl reference electrode CONCLUSION New neutral complex compounds of Ru(III) with N-phenyl-5-X-salicylideneimine (where X = Cl or Br) and N-donor heterocyclic ligands (pyridine or pyrimidine) were synthesized. On the basis of mass spectra, CHN analysis, infrared and electronic spectra, the complexes were formulated as [RuCl(N-Ph-5-X-salim)2B] where X = Cl for B= py and X = Br for B = pym. In the octahedral environment coordination of the Ru(III) to the imine nitrogen and phenolic oxygen atoms of the Schiff bases and nitrogen atom of py or pym occurred. MALDI TOF/TOF mass spectrometry confirmed existence of the neutral molecules. Redox property of complexes has been determined using cyclic voltammetry.

REFRENCES Akalin, E., Akyuz, S. (2008). Vibrational study on

Zn(Pyrimidine)2Cl2, Pyrimidine-Al(OH)3 and Pyrimidine-(Al(OH)3)2 complexes. Vibrational spectroscopy, 48,233-237.

Bayari, S., Ataç, A., Yurdakul, Ş. (2003). Coordination behaviour of nicotinamide: an infrared spectroscopic study. Journal of Molecular Structure, 655, 163-170.

Begic-Hairlahovic, S., Kahrovic, E., Turkusic, E. (2014). Synthesis, characterization and interaction with CT DNA of novel cationic complex Ru(III) with indazole and Schiff base derived from 5-chlorosalicylaldehyde. Bulletin of the Chemists and Technologists of Bosnia and Herzegovina, 43, 15-20.

Blagus, A., Cincic, D., Friscic, T., Kaitner, B., Stilinovic, V. (2010). Schiff base derived from hydroxyarylaldehydes: molecular and crystal structure, tautomerism, quinoid effect, coordination compounds. Maced. J. Chem. Chem. Eng.,29 (2), 117-138.

Coluccia, M., Natile, G. (2007). Trans-Platinum Complexes in Cancer Therapy. Anti-Cancer Agents in Medicinal Chemistry, 111-123.

Dholakiya, P. P., Patel M. N. (2002). Synthesis, spectroscopic studies, and antimicrobial activity of Mn(II), Co(II), Ni(II), Cu(II), and Cg(II) complexes with bidentate Schiff bases and 2,2′- bipyridilamine. Synthesis and Reactivity in Inorganic and Metal-Organic Chemistry, 32(4), 753-762.

EjidikeI. P., Ajibade p. A. (2016). Synthesis, Characterization, Anticancer, and Antioxidant Studies of Ru(III) Complexes of Monobasic Tridentate Schiff Bases. Bioinorganic Chemistry and Applications, vol. 2016, 11 pages.

Kahrovic , E. (2014). Ruthenium Compounds with Schiff Bases: Design and Promising Applications of Salicylideneimine Complexes. In Keeler, G. P. (Ed.), Ruthenium: Synthesis, Physicochemical Properties and Applications.,(p.p. 269-283).NOVA Publishers.

Kahrovic, E., Bektas, S., Turkusic, E., Zahirovic A. (2014). Evidence on antimicrobial activity of Sodium dichloro-bis[N-phenyl-5-chlorosalicylideneiminato-N,O]ruthenate(III) against gram positive bacteria. SYLWAN, 158(5), 482-493

Kahrovic, E., Dehari, S., Dehari, D., Begic, S. and Ljubijankic, N. (2010). Synthesis and characterization of new Ru (III) complexes with monobasic (NO) and dibasic (ONO) Schiff bases derived from salicylaldehydes. Technics Technologies Education Management 5/4, 799-803.

Kahrovic, E., Turkusic, E. (2012). New Ruthenium Complexes with Schiff Bases as Mediators for the Low Potential Amperometric Determination of Ascorbic Acid, Part II: Voltametric and Amperometric evidence of mediation with Bromo-derivative of Tetraethylamonium dichloro-bis[N-

-1.200 -1.100 -1.000 -0.900 -0.800 -0.700 -0.600 -0.500 -0.400 -3 -0.400x10 -3 -0.300x10

-3 -0.200x10

-3 -0.100x10 0

-3 0.100x10 -3 0.200x10

E / V

i / A

2 1

32 Begić et al.

phenyl-5-halogeno-salicylideniminato N,O]ruthenat(III).HealthMED, 6/3, 1046-1049.

Kahrovic, E., Turkusic, E., Zahirovic, A. (2014). Calf Thymus DNA Intercalation by Anionic Ru(III) Complexes Containing Tridentate Schiff Bases Derived from 5-X-Substituted Salicyladehyde and 2-Aminophenol. Journal of Chemistry and Chemical Engeenering, 8, 335-343.

Kahrovic, E.,Turkusic, E., Ljubijankic, N., Dehari, S., Dehari, D., Bajsman, A. (2012). New Ruthenium Complexes with Schiff Bases as Mediators for the Low Potential Amperometric Determination of Ascorbic Acid, Part I: Voltametric and Amperometric evidence of mediation with Tetraethylamonium dichloro-bis[N-phenyl-5-chloro-salicylideniminato-N,O]ruthenat(III). HealthMED, 6/2, 699-702.

Keppler, B. K., Henn,M., Juhl, U. M., Berger, M. R., Niebl, R., Wagner, F. E. (1989).New ruthenium complexes for the treatment of cancer. In Ruthenium and Other Non-Platinum Metal Complexes in Cancer

Chemotherapy (p.p. 41-69). Springer Berlin Heidelberg.

Ljubijankic, N., Zahirovic, A., Turkusic, E., Kahrovic, E. (2013). DNA Binding Properties of Two Ruthenium (III) Complexes Containing Schiff Bases Derived from Salicylaldehyde: Spectroscopic and Electrochemical Evidence of CT DNA Intercalation. Croatica Chemica Acta, 86(2), 215-222.

Turkusic, E., Kahrovic, E. (2012). Development of new low potential amperometric sensor for L-cysteine based on carbon ink modification by Tetraethylamonium dichloro-bis[N-phenyl-5-bromosalicylideniminato-N,O]ruthenat(III). Technics Technologies Education Management, 7/3, 1300-1303.

Quin, L. D., Tyrell, J. A., Fundametals of Heterocyclic Chemistry, Importance in Nature and in the Synthesis of Pharmaceuticals, John Wiley & Sons, 2010.

Summary / Sažetak

Sintetizirana su dva nova neutralna kompleksna spoja Ru(III) sa Schiff-ovim bazama i N-donorskim heterocikličnim

ligandima. Na bazi MALDI TOF/TOF masenih spektara, CHN elementarne analize, infracrvenih i elektronskih spektara

sintetizirani spojevi su formulirani kao [RuCl(N-Ph-5-X-salim)2B], gdje je X = Cl za B = Py i X = Br za B = Pym. U

oktaedarskom okruženju Ru(III) bidentatna Schiff-ova baza djeluje kao anionski ligand gdje se koordinacija ostvaruje

preko deprotoniranog fenolnog atoma kisika i azometinskog atoma azota. Koordinacija monodentatnih N-donorskih

heterocikličnih liganada se odvija preko slobodnog elektronskog para na atomu azota.

The phosphate removal efficiency electrocoagulation wastewater

using iron and aluminum electrodes

Đuričić, T.a,*

, Malinović, B.N.a, Bijelić, D.

b

aUniversity of Banja Luka, Faculty of Technology, Stepe Stepanovica 73, 78000 Banja Luka,

2JP Dep-ot, Bulevar Živojina Mišića 23, 78000 Banja Luka, B&H

INTRODUCTION

Phosphorus is a natural nutrient, unavoidable in surface

water which appears almost always as a phosphate ion

(House, 2012). Although precious nutrient highly

valuable for the agriculture, phosphate released in

extensive quantities into the surface waters, causes

euthrofication.

Removal of the phosphate from the drinking waters and

also from the wastewater started to draw the attention of

the scientists and professionals from the 1960s

(Vasudevan, Sozhan, Ravichandran, et. al., 2008). As a

perspective methods have proven electrochemical

methods of wastewater treatment (El-Shazly, Daous,

2013). The electrochemical technologies have attracted a

great deal of attention, because of their versatility, which

makes the treatment of liquids, gases and solids possible

and environmental compatibility. These methods are

electrodialysis, electrooxidation, electroflocculation and

electrocoagulation (Miranzadeh, Rabbani, Dehqan, 2012).

This research is focused on the electrocoagulation

treatment of synthetic wastewater loaded with

phosphates. There are numerous studies on the removal of

phosphorus from wastewater. Some of the studies related

to electrochemical technology for wastewater treatment.

Electrocoagulation process of wastewater, and

consequently water containing phosphates, has become

very attractive for research in the last two decades.

Initially, the research was based on the research of

feasibility and applicability of the process of

electrocoagulation treatment of wastewater containing

phosphates to the testing of the basic parameters such as

current density, electrolysis duration, temperature, initial

phosphate concentration, the type and concentration of

supporting electrolyte and their optimization (Behbahani,

Moghaddam, Arami, 2010; Shalaby, Nassef, Mubarak, et.

al., 2014; Bektas, Akbulut, Inan, et. al., 2004; Irdemez,

Yildiz, Tosunoglu, 2006; Irdemez, Demircioglu, Yildiz,

2006; Irdemez, Demircioglu, Yildiz, et. al. 2006). After

that was carried studies with different types of electrodes

such as mild steel, stainless steel, aluminum ore, zinc,

copper, etc. and was carried to compare them

(Vasudevan, et. al., 2008; Vasudevan, Lakshmi, Jayaraj,

et. al., 2008; Vasudevan, Lakshmi, Sozhan, 2009; Hong,

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Keywords: Electrode material, phosphate removal,

electrolysis duration

*Corresponding author: E-mail: [email protected] Phone: +387 65 334 067

Fax: +387 51 434 351

Abstract: Effects of electrolysis duration, initial phosphate concentrations and supporting

electrolyte concentrations on the phosphate removal efficiency by electrocoagulation

using either aluminum or iron electrodes were investigated in this study. All experiments

were performed in a batch electrochemical reactor on synthetically prepared wastewater of

the initial volume 0.2 L. The results indicate that increase of initial phosphate

concentration has reduced removal rate, and by increasing the electrolysis duration

removal efficiency increases. It was found that the aluminum electrode has higher removal

efficiency (98.9%) compared to the iron electrode (93.5%) for 40 minutes of treatment

(pH=3, j=1 mA/cm2, γ0=50 mg/L P–PO4). The addition of supporting electrolyte

( NaCl=0.25 g/L) is achieved removal efficiency of 50.2% for Fe and 52.1% fo Al electrode

in only 10 minutes of treatment, respectively.

34 Đuričić et al.

Chang, Bae, et. al., 2013). Process performance was

achieved as in the batch reactor (Attour, Tuoati, Tlili, et.

al., 2014), in a flow reactor with recirculation (El-Shazly,

Daous, 2013; Lacasa, Canizares, Saez, et. al., 2011), as

well as in reactors which combine the electrocoagulation,

electroflotation and the electrooxidation process (El-

Masry, Sadek, Mekhemer, 2004).

Attour et al. in their research indicate that the phosphate

removal efficiency from wastewater at Al electrode was

90% after 15 minutes of treatment. The highest removal

of phosphate was obtained at a initial concentration of

phosphate 50 mg/L, as an supporting electrolyte used is

4,5 mM NaCl and at a current density 1 mA/cm2 (Attour

et al., 2014). A comparison between the phosphate

removal efficiency by using Al and Fe electrodes are

investigated Behbahani et al. At pH=3 are achieved

maximum removal efficiency, which amounts to 100%

for Al electrodes and 84.7% for Fe electrodes. This

highest removal efficiency at a pH=3 was obtained at a

initial phosphate concentration of 400 mg/L and at a

current density of 250 A/m2 for both types of electrodes.

The turbidity of the solution is higher for Fe electrode. In

the sludge was found a greater amount of PO43-

for Al

electrode, which indicate higher removal efficiency

(Behbahani et al., 2010). During phosphate removal from

the wastewater from the process of phosphating with zinc

phosphate, pH 3 has proved as most effective. The

highest phosphate removal was for 15 minutes of

electrolysis. At optimal current density of 60 A/m2 was

removed 97.8% phosphate from wastewater (Kobya,

Demirbas, Dedeli, 2010). In a series of research Irdemez

et al. examined the impact of the initial phosphate

concentration, current density and pH, as the main

parameters of electrocoagulation Removal efficiency and

reaction rate decreases with the increase of initial

concentration of phosphate. According to their results, the

removal efficiency increases with current density,

therefore increasing energy consumption. It was found

that the optimal initial pH-value of the wastewater is 3

(Irdemez et. al. 2006, Irdemez et. al. 2006, Irdemez et. al.

2006).

EXPERIMENTAL

Experimental part of the research is contained by the

application of electrocoagulation proces for removing of

phosphates from simulated wastewater. Experimental

setup is shown on the Figure 1. The batch electrochemical

reactor of 250 cm3 capacity made from polypropylene

with constant mixing was used, combined with two

electrodes of the same area surface. The dimensions of

the electrodes are 40x40x1 mm. The total effective area

of one electrode is 17.2 cm2, and the distance between 2

cm. Electrodes were connected to digital power source

(Atten, APS3005SI; 30V, 5A) with the potentiostatic and

galvanostatic operating options.

Figure 1. Schematic view of electrochemical reactor. 1 – source of

electric power; 2 – anode; 3 – cathode; 4 – magnetic stir bar;

5 – electrochemical cell; 6 – magnetic stirrer)

All the experiments were performed at an ambiental

temperature of sample and initial volume synthetic

wastewater of 200 cm3. Before each treatment electrodes

were cleaned and degreased and the current density was

set to a certain value. Used chemicals are of p.a. purity. In

order to prepare synthetic wastewater of the particular

concentration was used commercially available 99.3%

potassium dihydrogen phosphate (KH2PO4), „Kemika“,

Croatia. As supporting electrolyte was used 99.5%

sodium chloride (NaCl), „Lachner“, Czech Republic. As

an electrode material was used steel (EN 10130-91; max.

0.08% C, max. 0.12% Cr, max. 0.45% Mn, max. 0.60%

Si) and aluminum (Al 99.5/EN AW-1050 A; max. 0.25%

Si, max. 0.40% Fe, max. 0.05% Cu, max. 0.05% Mn,

max. 0.05% Mg, max. 0.05% Ti, max. 0.07% Zn, min.

99.50% Al), which meet the required standards.

Before each experiment, in accorcdance to the literature

data, was adjusted the optimum pH-value of the synthetic

wastewater (pH=3) with HCl (Irdemez et. al. 2006,

Behbahani, et. al., 2010, Kobya, et. al., 2010). Treated

synthetic wastewater after each experiment was collected

and filtered through filter paper „Filtres Fioroni“, France

(Ref.:0015A00007; size:125 mm; qty.: 1000). Prepared

samples of synthetic wastewater before and after

treatment were analyzed on the following parameters:

phosphorus content (P-PO4), total dissolved substances

(TDS ), pH, electrolyte resistivity (ρ) and conductivity

(κ). TDS, ρ and κ are determined on the multimeter

(Consort C861), and a phosphorus concentration

spectrophotometrically (λmax=410 nm) in the UV-VIS

spectrophotometer (Perkin-Elmer, LAMBDA 25)

according to a standard method (APHA, 1999). The

composition of the resulting precipitate is determinate

using the FTIR spectroscopy (Bruker, Tensor 27).


Results for the phosphate removal by electrocoagulation

are expressed by mass concentration (mg/L) and

phosphate removal efficiency, Eu, in percent calculated

by the followed equation:

%100i

fi

UE (1)

There the γi and γf are the initial and the final

concentration of the phosphate in mg/L.


Figure 2 show the influence of the electrolysis duration

(2.5; 5; 10; 30; 40 min) on the reduction of phosphate

concentration by using iron and aluminum electrodes,

respectively. Used current density was j=1 mA/cm2

which, in accordance to the literature, was cited as the

optimal current density (Attour et al., 2014). Experiments

were performed without the presence of supporting

electrolyte and the initial concentration of phosphate

γ0=50 mg/L. It can be seen that the aluminum electrode

has a slightly greater removal efficiency (98.9%)

compared to the iron electrode (93.5%), for 40 minutes of

treatment, which is in accordance with previous studies

(Behbahani, et. al., 2010).

Figure 2. Phosphate concentration in relation to the electrolysis

duration for iron and aluminum electrodes (j=1 mA/cm2, t=40 min,

γ0=50 mg/L, pH=3)

The influence of the concentration of supporting

electrolyte on the phosphate removal efficiency for both

electrode pairs was examined in previous research and

shown in Figure 3 (Malinovic, Djuricic, Bjelajac, 2016).

It was found that the optimal concentration of supporting

electrolyte γNaCl=0.25 g/L, which is significantly lower

concentration compared to the previously published

recommendations (Attour et al., 2014; Kuokkanen,

Kuokkanen, Ramo, 2014). For the case of aluminum

electrodes, highest removal efficiency at a concentration

of γNaCl=0.25 g/L was Eu=52.1%, while for the iron

electrode highest removal efficiency was Eu=50.2% at a

same experimental conditions.

Figure 3. Removal efficiency in relation of concentration of NaCl

(j=1 mA/cm2, t=10min) (Malinovic, et. al. 2016)

By increasing the initial phosphate concentration in

wastewater removal efficiency linearly decreases in the

case of the both electrode pairs (Malinovic, Atlagic,

Malinovic, 2016; Malinovic, Malinovic 2016) (Figures 4

and 5). At optimum current density j=0.25 mA/cm2 19

and optimum concentration of supporting electrolyte

γNaCl=0.25 g/L removal efficiency is increased by

reducing initial phosphate concentration in wastewater

(25, 50 i 100 mg/L), which is in accordance with with

previous researche (Irdemez, et. al. 2006). Efficiency

was the highest at initial phosphate concentration γ=25

mg/L and amounts Eu=62.6% (Al), Eu=63.4% (Fe). The

lowest removal efficiency was at initial phosphate

concentration γ=100 mg/L and amounts Eu=29.8% (Al)

and Eu=30.3% (Fe).

Figure 4. Removal efficiency in relation of initial phosphate

concentration in wastewater, for Al electrodes (j=0.25 mA/cm2,

γNaCl=0.25 g/L, t=10 min) (Malinovic, et. al. 2016)

Figure 5. Removal efficiency in relation of initial phosphate

concentration in wastewater, for Fe electrodes (j=0,25 mA/cm2,

γNaCl=0,25 g/L, t=10 min) (Malinovic, et. al. 2016)

In some of the previous studies electrocoagulation

phosphate was researched the kinetics of the process,

where concluded that the process follows a first order

reaction kinetics (El-Shazly, Daous, 2013; Shalaby,

Nassef, et. al., 2014; Zhang, Zhang, Wang, 2013). At the

experimental conditions (j=1 mA/cm2, γ0=50 mg/L,

pH=3), the dependence of ln(C0/C) of the electrolysis

duration (min) shows a linear relationship (Figure 6). It

can be concluded that the electrocoagulation process can

be described by a first order rate, which confirming the

relatively high correlation coefficient for iron

(R2=0.9837) and aluminum electrodes (R

2=0.9665).

Reaction rate constants of electrocoagulation phosphate


amounts k=-0.0745 min-1

(Fe) and k=-0.1212 min-1

(Al),

respectively. Based on the processed data (Figure 2),

equation 2 and 3 representing the dependence of the

phosphate concentration variation of the electrolysis

duration in electrocoagulation process using iron or

aluminum electrode.

xe 0745,02437,70 (2)

xe 1212,07590,93 (3)

Figure 6. The dependence of ln(C0/C) of the electrolysis duration

(j=1 mA/cm2, γ0=50 mg/L P-PO4, pH=3)

By using Fourier transform infrared spectroscopy (FTIR)

method, can be determine characteristics of the obtained

sediment. Using iron electrodes in the electrocoagulation

process it was obtained an amorphous precipitate of iron

hydroxide, and the sediment has a polymeric structure

(Inan, Alaydin, 2014). For the case of aluminum

electrodes, at acidic pH values, free Al3+

ion and

Al(OH)2+

hydroxocomplex species are predominate.

Precipitation of phosphate involves the dissolved cations

Al3+

and Fe3+

when iron or aluminum is present in the

water, FePO4·2H2O and AlPO4·2H2O or mixed Al(OH)3–

AlPO4 and Fe(OH)3–FePO4 form (Kobya, et. al., 2010).

In Figure 7 is shown capture FTIR spectroscopic analysis

of resulting sediment after the treatment of synthetic

wastewater by using aluminum electrodes.

Figure 7. FTIR absorption spectrum of the resulting sediment (Al

electrodes)

CONCLUSION

Using aluminum electrodes in the electrocoagulation

process results a higher phosphate removal efficiency

from the wastewater, in relation to the iron electrode. By

increasing the electrolysis duration decreases phosphate

concentration in wastewater. Based on the results,

recommended concentration of the supporting electrolyte

is concentration of 0.25 g/L. Increased removal efficiency

is achieved with lower initial concentrations of

phosphate.

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APHA, (1999). Standard Methods for Examination of

Water and Wastewater, American Public Health

Association.

Atour, A., Tuoati, M., Tlili, M., Ben Amor, M., Lapicque,

F., Leclerc, J.P. (2014). Influence of operating

parameters on phosphate removal from water by

electrocoagulation using aluminum electrodes.

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coagulation method. Water, Air and Soil Pollution,

158 (1), 373-385.

El-Shazly, A.H., Daous, M. (2013). A Kinetics and

performance of phosphate removal from hot industrial

effluents using a continuous flow electrocoagulation

reactor. International Journal of Electrochemical

Science, 8, 184-194.

Hong, K., Chang, D., Bae, H., Sunwoo, S., Kim, J., Kim,

D.G. (2013). Electrolytic removal of phosphorus in

wastewater with noble electrode under the conditions

of low current and constant voltage. International

Journal of Electrochemical Science, 8, 8557-8571.

House, C. D. (2012). Standard methods for the

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Irdemez, S., Demiricioglu, N, Yildiz, Y.S., Bingul, Z.

(2006). The effects of current density and phosphate

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52, 218-223.

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(2010). Treatment of rinse water from zinc phosphate

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processes. Journal of Hazardous Materials, 173 (1-3),

326-334.

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Summary/Sažetak

U ovom radu ispitan je uticaj vremena trajanja elektzrolize, početne koncentracije fosfata i koncentracije pomoćnog

elektrolita na efikasnost uklanjanja fosfata elektrokoagulacijom, primjenom aluminijumskih ili željeznih elektroda. Svi

eksperimenti su izvedeni u šaržnom elektrohemijskom reaktoru na sintetički pripremljenoj otpadnoj vodi početnog

volumena 0.2 L. Rezultati pokazuju da sa porastom početne koncentracije fosfata smanjuje se efikasnost uklanjanaja, a sa

porastom vremena trajanja elektrolize raste i efikasnost uklanjanja fosfata. Aluminijumske elektrode pokazuju veću

efikasnost uklanjanja (98.9%) u poređenju sa željeznim elektrodama (93.5%) u toku 40 minuta tretmana (pH=3, j=1

mA/cm2, γ0=50 mg/L P–PO4). Dodatkom pomoćnog elektrolita ( NaCl=0.25 g/L) postignuta je efikasnost uklanjanja od

50.2% za Fe, odnosno 52.1% za Al, za samo 10 minuta tretmana.

Mathematical modeling and simulation of the composting process

in a pilot reactor

Papračanin, E.*, Petric, I.

Department of Chemical Engineering, Faculty of Technology Tuzla, University of Tuzla, Univerzitetska 8, 75000 Tuzla,

Bosnia & Herzegovina

INTRODUCTION

Waste as a result of human activities which in the

modern age, as cities developed, is a serious problem.

Waste collection and disposal is necessary, as for

hygiene and space reasons, because large quantities of

solid waste cover huge areas (Atalia et al., 2015).Waste

management is a critical area in practice with regard to

increase of pollution. Management techniques for

organic waste are carried out in many ways: waste

disposal in landfills, incineration, pyrolysis and

gasification, composting and anaerobic digestion

(Taiwo, 2011).According to the environmental laws of

the European Union, organic waste in landfills will have

to be reduced to a certain extent, it implies a choice of

one of several methods for the treatment of the organic

portion of municipal solid waste, of which the cheapest

and most effective is composting process.

Composting is a complex biological process, in which

the heterogeneous organic waste is converted into humus

(compost), under the influence of mixed microbial

populations in controlled conditions of moisture,

temperature and aeration. The composting process can

be characterized as a way of sustainable waste

management (Bonoli et al.,2012), and the resulting

compost can be used to improve soil quality, and can be

used for fertilization and soil conditioning (Zaha et al.,

2011; Zorpas e tal., 2000).

In order to develop a process that would lead to a more

efficient degradation of organic matter and reduce the

negative impact of waste on the environment,

mathematical modeling provides great opportunities for

simulation and optimization of processes. Mathematical

modeling provides the understanding of dynamic

interaction between the mechanisms and provides the

basis for a rational design process (Higgins and Walker,

2001). By the literature review, it can be said that most

of the so far published models based on solving

equations of heat balance (Bach et al., 1987; Kishimoto

et al., 1987; Nakasaki et al., 1987; Haug, 1993; Van Lier

et al., 1994; Kaiser, 1996; Stombaugh and Nokes, 1996;

Das and Keener, 1997; Mohee et al., 1998; Seki, 2000;

Scholwin and Bidlingmaier, 2003; Xi et al., 2005) and

massbalance (Keener et al., 1993; Kaiser, 1996;

Stombaugh and Nokes, 1996; Das and Keener, 1997;

Mohee et al., 1998; Higgins and Walker, 2001). Most of

the researchers observed a system for composting at the

macro level, with the emphasis on reactor system




2017

47

39-48 UDC: __________________________




Keywords: mathematical modeling

simulation

composting process kinetics

pilot reactor


Phone: 00-387-35-320-806

Abstract: In this paper, a mathematical model for composting process with an

engineering approach was presented. The model describes the n-th order kinetics of

composting process (mesophilic-thermophilic phase) with mass and heat balances in the

process. Verification of the model was performed using experimental data obtained from a

pilot reactor. Measured dynamic state variables used for a verification of the model were:

organic matter mass, water mass in a mixture, amount of oxygen and carbon dioxide,

temperature of mixture and the temperature of gas phase. The developed mathematical

model was implemented in numerical software package MATLAB. Three kinetic

parameters were estimated using the Marquardt method. Global sensitivity analysis and

statistical F-test showed that the model is valid for predicting the change in five dynamic

state variables. The advantage of the model is that it can be applied to the composting

process with mixtures of different compositions in reactors with different volumes.


40 Parpačanin et al.

(Nakasaki et al., 1987; Hogan et al.,1989; Keener et al.,

1993; Van Lier et al., 1994; Richard et al., 1999;

Robinzon et al., 2000; Barrena et al., 2005; Nakayama et

al., 2007; Mason, 2008). Physical modeling of processes

which helps identifying process in many aspects in

laboratory and pilot scale. Opposite the physical

modeling, mathematical modeling enables prediction of

characteristics of the process, based on the knowledge of

independent data on the measured variables and process

conditions. Verified mathematical model predicts, within

certain limits, the characteristics of the process in the

laboratory, pilot and full scale (Mason, 2007). The

authors who have applied engineering approach, set the

reactor in the focus of research, during which they

modeled the process that involves mass balance, heat

balance, as well as other processes occurringin vivo.

Some of the authors who applied engineering approach

are: Nakasaki et al., (1987), Hogan et al., (1989),

Keener et al., (1993), Van Lier et al., (1994), Richard et

al., (1999), Robinzon et al., (2000), Themelis and Kim

(2002), Cronje et al. (2004), Barrena et al., (2005),

Mudhoo and Mohee (2006, 2007), Nakayama et al.,

(2007), Mason (2008), Kumar et al., (2009), Zhanget al.,

(2010), Baptista et al., (2010), Petric et al., (2015),

Shishido and Seki (2015). The thing that the researchers

dealt with the last thirty years, is corrective functions for

temperature, free space for air, moisture content and

oxygen concentration. During literature review, the most

important and most modeled corrective function is

related to temperature. General review of corrective

function for temperature, can be found in Mason (2007).

A large number of the researchers modeled composting

process with first order kinetics. However, models with

first order kinetics are greatly simplified and the results

do not agree very well with the data from the

experiment, especially in the case of very heterogeneous

systems. Very few of researchers described the

degradation of the substrate, with kinetics n-th order

kinetics: Briški et al.,(2003a, 2003b, 2007); Petric and

Selimbašić (2008); Barneto et al.,(2010); Avdihodžić et

al.,(2011). In their work Barneto et al.,(2010), gave an

explanation why the composting process can notbe

predicted first order kinetics. The objectives of this study

are related to possibilities of performing process of

aerobic composting of municipal solid waste in pilot

reactors, development of an integrated kinetic and

reactor mathematical model to describe the

decomposition of organic matter, mass transfer and heat,

based on existing models, then optimization of kinetic

parameters of the proposed model based on experimental

data for several dynamic state variables obtained from

the pilot reactor and at the end verification and

validation of the developed mathematical model, with

independent data and testing the sensytivity of the

model.

MATERIALS AND METHODS

In the experiments have been used a specially designed

pilot reactors (volume 57 dm3). Pilot reactors are made

of high density polyethylene, following dimensions:

height 686 mm, outer diameter 330 mm, wall thickness

4.8 mm. Reactors are thermally insulated with a layer of

foamed polyethylene thickness of 10 mm. The reactors

were equipped with two air inlets and a few aperture for

the taking samples. Two taps are attached on the cover

of reactors, one tap is used for the continuous exit gas

mixture, and the second tap is used for measuring the

concentration of carbon dioxide and oxygen in the

reactor outlet.

Composting material

Organic fraction of municipal solid waste (OFMSW)

were used in the experiment, also with poultry manure,

wood chips, waste yeast from the beer industry. Basic

physical and chemical characteristics of the material are

shown in Table 1. The basic material used in the

experiment were OFMSW, which is synthesized by

mixing the food waste (42.1 mass.%), paper and

cardboard(31.6 mass.%) and garden waste (26.3

mass.%)

Table 1. Basic physical and chemical characteristics of OFMSW,

poultry manure, wood chips, waste yeast

Material Moisture

content (% w.b.)

Organic

Matter

(%d.b.) pH

Electrical condutivity

(dS m-1

)

C/N ratio

OFMSW 72.44 88.17 6.70 1.790 52.73

Poultry

manure 77.03 75.13 7.53 2.317 5.83

Wood

chips 10.03 99.90 5.31 0.240 77.19

Waste

yeast 95.61 91.55 6.46 2.795 21.19

w.b. – wet base, d.b. – drybase

Food waste is collected from restaurants in the Student

Center of the University of Tuzla and the main city

market in Tuzla. Garden waste which is used in the

experiment collected from the city's parks and home

gardens in Tuzla. The paper that was used in the

experiment, consisting mainly used office paper

collected at the Faculty of Technology in Tuzla.

Cardboard that was used in the experiment, collected

from several shopping centers in Tuzla. The role of

poultry manure is to adjust the ratio of C/N, and to act as

inoculum. Sawdust is added to increase aeration of

mixture in reactors. Table 2 shows the percentage

composition of the initial mixtures used in the reactors,

and Table 3 shows the physical-chemical composition of

the mixture for composting.

Table 2. Percentage composition of the starting mixture (mass.%)

in the reactors

Reactor OFMSW Poultry manure Wood

chips

Waste

yeast

1 67.6 10.8 10.8 10.8

2 77.8 5.5 11.1 5.5

3 82.0 5.1 10.3 2.6


Table 3.Basic physical and chemical characteristics of starting

mixtures in reactors

Sampling and analysis

After daily mixing of the composting mixtures, samples

were taken from different places in the mass (top, middle

and bottom, three samples from each places) in order to

obtain a representative sample. Moisture content was

analyzed by dry oven method at 105°C for 24 h (APHA,

1995). The organic matter (OM) content (volatile solids)

was determined after burning in an oven at 550°C for 6 h

(APHA,1995). Conversion of organic matter (%) was

calculated from the initial and final organic matter mass,

according to the following equation:

( )100OTP OTK

OTP

m mK

m

1

mOTP – mass of organic matter at the beginning of the

process (kg),

mOTK – mass of organic matter at the end of the process

(kg). Determination of nitrogen is carried out by the Kjeldahl

method (Austrian Standard, 2001). The mass percentage

of carbon (%C) is calculated according to the following

equation (Haug, 1993):

%%

1.8

OMC

2

Concentrations of carbon dioxide and oxygen Infrared

Gas Analyzer MGA5, VarioPlus Industrial (MRU

GmbH, Germany) was used. The electrochemical cell of

the analyzer is used to measure the concentration of

oxygen in the range of 0 to 21.0% by volume, where the

accuracy of ± 0.2 vol.%.

Matematical modeling

All details about the mathematical model (description;

assumptions and simplifications; mass balances

equations for water, carbon dioxide, oxygen, ammonia

and nitrogen; heat balances equations for solid–liquid

and gas phases; other supporting algebraic equations,

explanations) can be found in the previous paper (Petric

et. al., 2015).

The focus in this paper will be on n-thorder kinetics and

on effect of temperature on reaction rate. The n-th order

kinetics was applied, where the reactionrate is equal to

the rate of organic matter degradation:

nOT

OT mkdt

dm

3

Reaction rate constant is the function of temperature,

oxygen concentration, pH, moisture content and free air

space:

SPZOHpHOT kkkkkk22

4

As a basis for the description of the effect of temperature

on reaction rate constants, modified Arrhenius's

expression were used, (Fogler, 2005; Sheridan et al.,

2012):

1 1

293

E

R TTk A e

5

wherein:

A– frequency factor (units depending on the order of

reaction),

T – thermodynamic temperature of the substrate (K),

E – activation energy (J/kmol),

R– universal gas constant (J/kmol∙K).

This model has been proposed a modified form of the

expression (5):

1 1

293 TTk e 6

wherein:

=A i =E/R kinetic constants that need to be

determined, together with the reaction order n in the

expression (3). Other equations for correction factors in

equation (4) and constants used in model are given in

previous paper (Petric et. al., 2015). Stoichiometric

coefficient are determinated later in the paper.

Mathematical model consists of twelve nonlinear

ordinary differential equations with twelve dynamic state

variables (massof organic matter, mass of dissolved

oxygen, mass of dissolved carbon dioxide, mass of

dissolved ammonia, mass of water in the composting

mixture, mass of oxygen in gas phase, mass of

carbondioxide in gas phase, mass of ammonia in gas

phase, mass of water vapor in gas phase, mass of

nitrogen in gas phase, temperature of gas phase,

temperature of solid–liquid phase) and corresponding

algebraic equations. All differential equations and

algebraic equations are mutually connected and

nonlinear and therefore, they have to be solved

simultaneously both for the purpose of model calibration

and numerical simulation.

Four different categories of data are required in the

model: initial values of the dynamic state variables,

constants (physical, thermodynamic and stoichiometric),

kinetic parameters and operational conditions.

Calculation of initial mass values for dissolved oxygen,

carbon dioxide and ammonia in interstitial water was

based on solubility data from literature (Perry and

Green,1997). Initial mole values of gases were

calculated using the initial values of molar flows of

gases, airflow rate and volume of gas phase.

Applied numerical methods and software

Algorithms for parameter estimation and numerical

simulation were implemented in numerical software

packages Matlab and Polymath. For numerical solution

of system of differential equations, ODE23s solver,

modified Rosenbrock method (Shampine and Reichelt,

Reactor

Moisture

content (% w.b.)

Organic

matter(%

d.b.) pH

Electrical Conductivity

(dS m-1

)

C/N ratio

1 71.09 90.00 6.72 1.299 71.09

2 63.09 92.96 6.80 1.303 63.09

3 65.65 89.27 6.98 1.280 65.65


1997). In order to determine the kinetic parameters,

Marquardt method (Marquardt, 1963) was used with

application of experimental data. As a criterion of

agreement between values obtained by model and

experimental data, the following target function F was

taken as:

2

,mod ,

1 1

m n

j ij el ij eksp

j i

F W Y Y

7

wherein:

Wj – weight coefficient,

Yij,model – value of dynamic state variables obtained by

the model,

Yij,eksp – value of dynamic state variables obtained by

experiment.

After optimization of the kinetic parameters and the

order of reaction, stoichiometric coefficients for oxygen,

carbon dioxide and water and amount of loss of

conductive-convective heat were adjusted. Experimental

data used for verification of the model are: substrate

temperature, temperature of the gas phase, carbon

dioxideand oxygen concentration, mass of organic

matter, mass of water in the substrate. The program

consists a main file and three sub-routines and one file

with experimental values of the dynamic state variables.

The main program is used to call experimental data

required for optimization, then vector of initial

conditions for independent and dependent variables, as a

vector of initial assumptions of parameters that need to

be optimized. The main program also performs a

statistical analysis by calling one of the routines for

Statistics and the output is the optimization results

numerically and graphically. Linear, multiple linear and

nonlinear regression was performed in the software

package Polymath. Regression is performed in order to

obtain of approximate values for the initial vector

parameters which are optimized using known values for

corrective function of temperature and the experimental

data. Sensitivity analysis was performed with the aim of

evaluation relative importance of selected model

parameters. Global sensitivity analysis was performed by

determination the absolute and relative sensitivity of

parameters. Details can be found in the previous paper

(Petric et. al., 2015). F-statistical test, as an indicator of

the sensitivity of the model was performed.


Optimization of kinetic parameters

Numerical optimization presents a serious challenge in

systems, such as the composting process. Problems that

occur during optimization of these "live" system usually

related to problems of initial conditions. First of all it is

necessary to select an appropriate numerical method that

would successfully solve a system of several differential

equations and then even further to optimize the unknown

parameters. As initial values for the of dynamic state

variables in the model, experimental values are used for

one of the reactors (reactor 1). The complex biochemical

systems and their modeling represent the systems that is

basically very difficult to simulate and optimize. The

problem of initial conditions for the parameters is solved

so that the used data from previous research Avdihodžić

(2011). Table 4 showes optimized values of kinetic

parameters together with the mean-square deviation.

Comparing the value of the standard deviation with the

previous work (Petric et al., 2015), valued at 0.4958, we

can conclude that the selected corrective function of

temperature gives significantly better results in the

optimization of parameters.

Table4.Optimized values of kinetic parameters in the

mathematical model

Parameter Value Unit

9.6595∙10-5

kg1-n

h-1

4.9140∙103

K

n 1.5526 -

*SD2 = 0.1020

Experimental data which has been used to evaluate the

model parameters, are shown in Figure 1. Swedish

chemist Arrhenius first proposed the dependence of the

reaction rate constant of temperature, which was later

confirmed through empirical and experimental data for a

large number of reactions, including reactions in

biological systems (Fogler, 2005). Several authors have

used the Arrhenius model for temperature correction,

which is a function of the reaction rate constants for the

processes of degradation: Benefield and Randall (1980),

Haug (1993), Mashauri and Kayombo (2002), Rousseau

et al., (2004), Marsili-Libelli and Checchi (2005),

Nitisoravut and Klomjek (2005), Andreottola et al.,

(2007), Kadlec (2009). Most of the authors used the

mentioned corrective function for the first order kinetics,

while in this study is used a model of n-th order. Model

of n-th order, is proposed based on the fact that the

organic waste consists many different organic

compounds which decompose with different rates and

different kinetics of reaction orderobtained value of the

activation energy is correlated with literature values.

Adjusted values for stoichiometric coefficients are:

Value for conductive-convective heat loss is 2650 J ∙ h-1

.

Sensitivity analysis

With the aim of testing the sensitivity of the model,

values of the absolute and relative sensitivity of model

parameters were calculated. Absolute and relative

sensitivity of the parameters is shown in Table 5. The

positive values of APS indicate that all three parameters

lead to an increase in the difference between the

experimental and simulation results. Sensitivity analysis

was performed for a small deviation of model

parameters from their optimal values (+1%). Based on

the RPS values it can be concluded that the most

sensitive parameter is reaction order and it has the

greatest impact on the formulation of the entire model.

Also, it is necessary to perform the validation of the

model from the point of view of data analysis. For this

purpose F-test was performed. F-test was performed


with the significance level = 0.05. Table 6 shows the

results of the F-distribution data.

4

4.5

5

5.5

6

6.5

7

7.5

8

0 100 200 300 400 500 600

Org

an

ic m

att

er m

ass

, k

g

Time, h a)

17.5

18.0

18.5

19.0

0 100 200 300 400 500 600

Ma

ss o

f w

ate

r, k

g

Time, h

b)

2.00E-05

2.50E-05

3.00E-05

3.50E-05

4.00E-05

4.50E-05

0 200 400 600

Am

ou

nt o

f O

2, k

mo

l

Time, h c)

0.00E+00

2.50E-06

5.00E-06

7.50E-06

1.00E-05

0 200 400 600

Am

oun

t of

CO

2, k

mol

Time, h

d)

270

280

290

300

310

320

330

340

0 100 200 300 400 500 600

Tem

per

atu

re, K

Time, h

e)

Figure 1. Experimental data from reactor 1: a) mass of organic

matter, b) mass of water in substrate, c) amount of O2, d) amount

of CO2, e) temperature of substrate.

The results of F-distribution show that the model is

acceptable for given conditions for the four dynamic

state variables. Only for the mass of water, the null

hypothesis is rejected. Performing a sensitivity analysis,

in terms of data for the mass of water in the substrate,

similar results get Neves et al. (2007). In the future, it is

necessary to perform a sensitivity analysis before the

optimization of kinetic parameters in order to obtain

more reliable simulation results.

Table 5. Parameters of the model and values of the sensitivity

analysis

Parameter

of the model Value APS RPS

α 9.65∙10-5

1147578 0.08932

β 4914.0 0.037635 0.14070

n 1.5526 22647.63 23.7114 aAPS-eng. absolute parameter sensitivity bRPS-eng. relative parameter sensitivity

Table 6. Results of the statistical analysis of data

Dynamic state

variable

a

exp

b

sim

Value

of

F-test

Testing the

hypothesis

Mass of

organic matter 0.7047 0.7422 1.0532

1.0532 ≤

2.0144

Mass

of water 0.0866 0.9848 11.366

11.366

2.0144

Amount

of O2 1.57∙10

11

2.24∙1011

1.4290 1.4290 ≤

2.0144

Amount

of CO2 5.52∙10

12

6.29∙1012

1.1385 1.1385 ≤

2.0144

Temperature

of gas phase 71.94 65.25 1.1026

1.1026 ≤

2.0144

Temperature

of substrate 71.94 65.62 1.0962

1.0962 ≤

2.0144

a-variance of experimental data

b- variance of simulation data

Verification of the model

Predictive simulation models are approximate imitation

of real systems, which can never accurately describe the

actual system. Verification of the model was performed

with data from two different reactors for five dynamic

variables of state (subsection 2.5). Verification of the

model with experimental data for the mass of organic

matter are shown in Figure 2, mass of the water in

supstrate are shown in Figure 3, the amount of gases

(CO2 i O2) are shown in Figure 4 and results for the

temperature of the substrate (measured values are the

same and the values calculated by model differ in the

maximum 0.5°C) are shown in Figure 5. A comparison

of experimental results and the numerical simulation

showed good agreement throughout the entire process

except when it comes to the mass of water in the

substrate, where they observed certain deviations. The

model shows a normal trend but experimental data

shows an unusual distribution. One of the reasons why

there is an unusual trend of experimental dat, is the


possibility of generation of air pockets in which there

has been a condensation of water vapor wich the samples

were taken.

Figure 2. Verification of the model for the mass of OM:

a) data from reactor 2, b) data from reactor 3.

a)

b)

Figure 3. Verification of the model for the mass of water in the

substrate: a) data from reactor 2, b) data from reactor 3.

Reaching the maximum temperature is the basis for the

efficiency of the composting process (Finstein i Morris,

1975; Finstein et al., 1986b) and during the period of

thermophilic degradation leads to the destroying largest

number of pathogens (Diaz, 2007). Temperature profiles

in pilot reactors can reliably predict the temperature

profiles in the processes taking place in full scale. This

fact is one of the advantages of this model. The fact that

the model well described experimental data confirms

that the model is valid for use on different experimental

conditions, which is one of the goals of mathematical

modeling. With small modifications, referring to the

initial conditions, this model can successfully simulate

the process of decomposition of organic solid waste with

different additions. No matter what kind of substrate is

used in the composting process, this model can well

predict the degradation process, especially in the active

stage of the process.

a)

b)

c)

d)

Figure 4. Model verification of the amount of gases: a) and b)

verification for amount of O2 for data from reactor 2 and 3. c) and

d) verification for amount of CO2 for data from reactor 2 and 3.

This conclusion can be justified by modeling the process

kinetics of n-th reaction order. Modeling the process of

n-th order kinetics, gives significantly better results

when it comes to degradation of organic matter from the

3

4

5

6

7

0 100 200 300 400 500 600

mO

T, k

g

Time, h

model

experiment

3

4

5

6

7

0 100 200 300 400 500 600

mO

T, k

g

Time, h

model

experiment

14

15

16

17

18

19

0 100 200 300 400 500 600

mH

2O

, kg

Time, h

modelexperiment

8

9

10

11

12

13

14

15

16

0 100 200 300 400 500 600

mH

2O

, kg

Time, h

model

experiment

3.00E-05

3.50E-05

4.00E-05

4.50E-05

5.00E-05

5.50E-05

0 100 200 300 400 500 600

Am

ount

of O

2, km

ol

Time, h

model

experiment

3.00E-05

4.00E-05

5.00E-05

6.00E-05

7.00E-05

0 100 200 300 400 500 600

Am

oun

t of

O2, k

mol

Time, h

modelexperiment

0.00E+00

5.00E-06

1.00E-05

1.50E-05

0 100 200 300 400 500 600

Am

ount

ofC

O2,

kmol

Time, h

model

experiment

0.00E+00

5.00E-06

1.00E-05

1.50E-05

0 100 200 300 400 500 600

Am

ou

nt

of

CO

2,

km

ol

Time, h

model

experiment


standpoint of biochemical processes that occur. This was

confirmed by Zhang et al., (2012) and Kulcu (2015).

a)

b)

Figure 5. Verification of the model for the temperature of the

substrate: a) data from reactor 2, b) data from reactor 3.

CONCLUSIONS

In this paper, the biggest problems related to

mathematical modeling of the process, during the

initiation of the developed mathematical model. The

problem of initial conditions is solved by using data

from previous studies. In the developed mathematical

model the activation energy for the degradation process

of waste were determinate (40855 kJ/kmol). Besides the

activation energy, frequency factor is estimated

(9.6595∙10-5

kg1-n

h-1

) and reaction order (1.55). Global

sensitivity analysis showed that variations of all three

parameters affect the increase in the difference in the

agreement between the model and experimental data.

Reaction order is most sensitive parameter. Results of

the F-distribution show that the model is acceptable for

given conditions with the level of significance = 0.05,

except of mass of water in the substrate. Verification of

the model was performed for five measured dynamic

state variables. The variety of initial conditions in two

reactors is important for the verification of the model.

The fact that the model very well describes experimental

data shows that the model is valid for use on different

experimental conditions. Advantage of this model is

reflected in the fact that with small changes, referring to

the initial conditions, can successfully simulate the

composting process of organic solid waste with different

additions. Heterogeneous systems, such as the material

used in this study are difficult to describe with constant

values stoichiometric coefficients for entire process in

the future should pay attention just to the values of

stoichiometric coefficients and their changes during the

process. In the future, should collect data from the plant

in full scale with the aim of checking the validity of the

proposed model.

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Summary/Sažetak

U ovom radu je predstavljen matematički model procesa kompostiranja sa inženjerskim pristupom. Model je

opisan kinetikom n-tog reda (mezofilno-termofilna faza) zajedno sa bilansom mase i topline u procesu.

Verifikacija modela je izvedena korištenjem eksperimentalnih podataka dobijenih iz pilot reaktora. Mjerene

dinamičke varijable, korištene za verifikaciju modela su: masa organskih tvari, masa vode u supstratu, količina

kisika i ugljičnog dioksida, temperatura smjese i temperature gasne faze. Razvijeni matematički model

implementiran je u numerički softverski paket MATLAB, pri čemu su određena su tri kinetička parametra

korištenjem Marquardt metode.Globalna analiza osjetljivosti i F-statistički test su pokazali da je model validan

za predviđanje pet dinamičkih varijabli stanja. Prednost ovog modela se ogleda u čijenici da se ovaj model

može primijeniti na proces kompostiranja smjesa različitog sastava u reaktorima različitih volumena.

Development and validation of the mathematical model for synthesis

of maleic anhydride from n-butane in a fixed bed reactor

Petric, I., Karić E.*

Department of Chemical Engineering, Faculty of Technology, University of Tuzla,

Univerzitetska 8, 75000 Tuzla, Bosnia and Herzegovina

INTRODUCTION

Maleic anhydride is a chemical compound with multiple

applications in the chemical industry. It is used for the

production of polyester and alkyd resins, additives for

lubricating oil, as the acid in the food industry, polymeric

materials etc. When n-butane replaced benzene as a raw

material for the production of maleic anhydride, it has

enabled the development of highly active and selective

catalyst. New process for the production of maleic

anhydride enabled higher yields with lower investments.

Higher yields of maleic anhydride are achieved with

oxygen concentration lower than its stoichiometric

amount. Among many industrial processes, partial

oxidation of n-butane to maleic anhydride has received a

special attention because of its economic profitability as

well as a high demand for maleic anhydride as chemical

product. Modern commercial processes for the production

of the maleic anhydride are based on a selective oxidation

of n-butane over a vanadium-phosphorus oxide catalyst in

fixed bed reactors and fluidized bed reactors (Dente et al.,

2003). Fixed bed reactor is well known technology,

whose improvement is based on the modification of

catalyst rather than design of a reactor. Cruz-Lopez et al.

(2005) investigated the selective oxidation of n-butane in

a membrane reactor. They used a high concentration of n-

butane. Fixed bed reactor is limited to processes with a

low concentration of n-butane (below 2%), while a fluid

bed reactor can operate with a higher concentration of n-

butane. Gascón et al. (2006) investigated the kinetics of

oxidation of n-butane to maleic anhydride over a

vanadium catalyst of commercial phosphorus under

aerobic and anaerobic conditions in the temperature range

from 400 to 435ºC. Fluid bed reactor has higher

efficiency for heat removal, better temperature control

and a lower yield of maleic anhydride compared to fixed

bed reactor. Diedenhoven et al. (2012) developed a model

for dynamics of phosphoric oxidation of n-butane to




2017

47

49-58 UDC: __________________________


Article info

Received:14/10/2016


Keywords:

modeling

n-butane

maleic anhydride

fixed bed reactor

simulation

kinetic models

Corresponding author: Ervin Karić E-mail: [email protected]

Phone: +387 60 321-5468

Fax: +387 35 320-741

Abstract: The aims of this study were the following: development of the mathematical

model for numerical simulation of partial oxidation of n-butane to maleic anhydride in a

fixed bed reactor and validation of developed mathematical model with real process data

from industrial reactor located in the Global Ispat Coke Industry Lukavac. Mathematical

model is consisted of differential equations that describe mass balances of each species,

energy balance, stoichiometry of reactions, pressure drop, kinetic model. Numerical

software package Polymath with Runge-Kutta-Fehlberg method was used for numerical

solution of differential equations. The developed mathematical model was validated with

three process data sets of five measured variables (temperature, pressure, concentration of

n-butane, concentration of carbon dioxide, concentration of carbon monoxide) and with

application of ten kinetic models from literature. Comparison of simulation results and

measured data showed a good agreement for three kinetic models. For the chosen kinetic

model, profiles of temperature, molar flows, conversion of n-butane and selectivity of

maleic anhydride were also presented.


50 Petric et al.

maleic anhydride with a vanadium-phosphorus-oxide

catalyst. The model showed that reverse sorption

processes determine the content of phosphorus in the

catalyst which is based on a vanadium-phosphorus oxide.

With addition of certain phosphorus concentration, the

loss can be compensated, while exceed of phosphorus

concentration can cause a complete deactivation of the

catalyst. Trifirò and Graseli (2014) analyzed the key

aspects for oxidation of n-butane to maleic anhydride

using a mixture of a vanadium-phosphorus oxide catalyst,

in order to determine relevant parameters required for

optimization of the catalyst.

Mathematical models provide a powerful tool for

simulation and design of process equipment in

chemical/process industry, as well as for determination of

the conditions of optimal performance. Results of these

studies can serve as useful guidelines for improving the

design and operation of the plants well as for improving

the performance of the whole process without a need for

expensive tests on the plant.

The aims of this study were the following: development

of the mathematical model for numerical simulation of

partial oxidation of n-butane to maleic anhydride in a

fixed bed reactor and validation of developed

mathematical model with real process data from industrial

reactor located in the Global Ispat Coke Industry

Lukavac.

MATHEMATICAL MODEL

The following assumptions and simplifications were

taken into account while developing the model:

- there are no radial gradients of temperature and

concentration in the reactor,

- a reactor operates in a steady-state,

- pseudo one-dimensional model is used,

- pressure drop coefficient is assumed,

- overall heat transfer coefficient is taken from the

reference Sharma et al. (1991).

The mechanism of reaction set I (in the case of

application of the kinetics models (12) and (13)).

The main reaction:

OHOHCOHCn 2432425.3104 (1)

Side reactions:

OHCOOHCn 25425.6104 2 (2)

OHCOOOHC 224

23

324 (3)

The mechanism of reaction set II (in the case of

application of the kinetics models (7), (8), (9), (10), (11),

(14), (15) and (16)).

The main reaction:

OHOHCOHCn 2432425.3104 (4)

Side reactions:

OHCOOHCn 25425.4104 (5)

OHCOOHCn 252

425.6104 (6)

The investigated kinetic models are:

Alonso et al. (2001):

O

M

O

B

BTR

C

C

C

C

Cer

26591

96.1653

11125000

1

O

M

O

B

BTR

C

C

C

C

Cer

26591

86.0653

11145000

2

O

M

O

B

MTR

C

C

C

C

Cer

26591

07.0653

11180000

3 (7)

O

M

O

B

BTR

C

C

C

C

Cer

12201

61.0653

11116000

1

O

M

O

B

BTR

C

C

C

C

Cer

12201

3.0653

11130000

2

O

M

O

B

MTR

C

C

C

C

Cer

12201

04.0653

11138000

3 (8)

O

M

O

B

BTR

C

C

C

C

Cer

12201

86.0653

1180000

1

O

M

O

B

BTR

C

C

C

C

Cer

12201

11.0653

1180000

2

O

M

O

B

MTR

C

C

C

C

Cer

12201

19.0653

1198000

3 (9)

skgcat

kmol

Buchanan and Sundaresan (1986):

O

M

O

B

BTR

C

C

C

C

Cer

26591

1096.1

125000

10

1

O

M

O

B

BTR

C

C

C

C

Cer

26591

1040.3

145000

11

2

O

M

O

B

MTR

C

C

C

C

Cer

26591

1070.1

180000

13

3 (10)


O

M

O

B

BTR

C

C

C

C

Cer

12201

1016.1

116000

9

1

O

M

O

B

BTR

C

C

C

C

Cer

12201

1050.7

130000

9

2

O

M

O

B

MTR

C

C

C

C

Cer

12201

1080.4

138000

9

3 (11)

skgcat

kmol

Centi et al. (1985):

BC

OCBCr

26161

2298.02616

410191.2

1

2298.0510028.72 OCr

151.1

6345.0

6

3 10989.4B

OM

C

CCr (12)

skgcat

mol

Marín et al. (2010):

O

M

O

B

BTR

C

C

C

C

Cer

24.12408.01

1073.3613

115.86515

4

1

O

M

O

B

BTR

C

C

C

C

Cer

24.12408.01

1076.8613

111.103293

5

2

OC

MC

OC

BC

MCTRer

24.12408.01

61311146052

41065.13

(13)

skgcat

mol

Lorences et al. (2003):

O

M

O

B

BTR

C

C

C

C

Cer

208141

17.2653

1154418

1

O

M

O

B

BTR

C

C

C

C

Cer

208141

34.1653

11104650

2

O

M

O

B

MTR

C

C

C

C

Cer

208141

19.0653

1166976

3 (14)

skgcat

kmol

Schneider et al. (1987):

B

O

O p

p

pr

2

15

2

15

5

1

1011.01

1011.01066.9

B

O

O pp

pr

5

55

2102.41

102.41072.1

B

O

O pp

pr

5

55

3102.41

102.41021.2 atm (15)

Sharma et al. (1991):

MB

TRpper 31011096.0

54.0673

1193100

6

1

54.0673

1193100

6

2 1015.0 B

TRper

2673

11155000

5

3 31011029.0 MM

TRpper atm (16)

A – n-C4H10, B – O2, C – C4H2O3, D – CO2, E – H2O, F –

CO

The molar balance of components is given by the

equation:

ii r

dW

dF (17)

where: i – component, Fi – molar flow of component i

(kmol/h), ri' – reaction rate for component i (kmol/(kg·h),

W – mass of catalyst (kg).

The heat balance is given by the equation:

n

i

pii

m

j

Rjijia

CF

HrTTaU

dW

dT

1

1

'

(18)

where: i – component, j – reaction, U – overall heat

transfer coefficient (kJ/(m2·h·K)), a – area of heat

exchange A per unit volume of reactor V (1/m), A –

surface of heat exchange (m2), V – volume of reactor

(m3), Cpi – specific heat capacity of component i

(kJ/(kmol·K)), Ta – ambient temperature (K), T –

temperature of the reaction mixture in reactor (K), rji' –

rate of the j reaction for the i component (kmol/(kg·h)),

ΔHRji – heat of the j reaction for the i component

(kJ/kmol).

Concentrations of components Ci in the reactions are

given by the equation:

0

0

0P

P

T

T

F

FCC

T

i

Ti (19)

where: i – number of component, CT0 – total

concentration of reaction mixture (kmol/m3), FT – total

molar flow of reaction mixture (kmol/h), Fi – molar flow

52 Petric et al.

of component i (kmol/h), T0 – inlet temperature of the

reaction mixture (K), T – temperature of the reaction

mixture in reactor (K), P0 – inlet pressure of the reaction

mixture (bar), P – pressure of the reaction mixture in

reactor (bar).

The total inlet concentration of reaction mixture CT0:

0

0

0TR

PCT

(20)

where: R – universal gas constant, (J/(mol·K)).

The total molar flow of reaction mixture FT: n

i

iT FF1

(21)

Relative rates of reaction in reaction j in compact

notation:

jk

jk

ji

ji rr (22)

where: j – reaction; i,k – component, υ – stoichiometric

coefficient, r – reaction rate.

The pressure drop is given by the equation:

0

0

0

02 T

T

F

F

P

P

P

T

T

dW

dP (23)

where: α – pressure drop parameter (1/kg).

The conversion of n-butane is given by the equation:

0

0

A

AAA

F

FFX (24)

where: FA0 – inlet molar flow of n-butane (kmol/h), FA –

outlet molar flow of n-butane (kmol/h).

The yield of maleic anhydride is given by the equation:

AA

CC

FF

FY

0

~ (25)

where: FC – outlet molar flow of maleic anhydride

(kmol/h).

The selectivity of maleic anhydride is given by the

equation:

E

C

CEF

FS~ (26)

where: FE – outlet molar flow of water (kmol/h).

Input data for mathematical model are: T0=431.15 K,

P0=134000 Pa, FA0=20.98 kmol/h, FB0=262.1 kmol/h,

FD0=0.4 kmol/h, FE0=6.72 kmol/h, Ta=683.15 K,

W=0.6359 kg, U=107 W/(m2·K), α=0.8 kg

-1, A=0.00035

m2, V=0.0013 m

3, a=0.26923 m

-1, R=8.314 J/(mol·K).

481085255.1

300004.0

20124025.0039.8575.1242655ˆ )(

TT

TTHI

R (J/mol) (27)

481007003.3

30000517.0

2013971.0939.4837.2646628ˆ )(

TT

TTHII

R (J/mol) (28)

48102141.1

30000119333.0

2026385.09.401428408ˆ )(

TT

TTHIII

R (J/mol) (29)

39102821.2

2410108.13313.0487.9

T

TTPA

C (J/(mol·K)) (30)

3810065.1

2510475.1

61068.3106.28

T

TTPB

C (J/(mol·K)) (31)

3810839.4

2410184.23484.0075.13

T

TTPC

C (J/(mol·K)) (32)

3810715.1

2510601.5

210343.7975.19

T

TTPD

C (J/(mol·K)) (33)

3910596.3

2510055.1

310923.1243.32

T

TTPE

C (J/(mol·K)) (34)

3810271.1

2510789.2

210285.1896.30

T

TTPF

C (J/(mol·K)) (35)

Numerical software package Polymath with Runge-Kutta-

Fehlberg method was used for a numerical solution of

differential equations.


Tables 1-3 show comparisons of the results of numerical

simulations with measured values of outlet process

parameters, differences between numerical simulations

results and measured values of outlet process parameters,

and percentage deviations of the numerical simulations

results from measured values of outlet reactor process

parameters. From October 2015, the reactor in the Global

Ispat Coke Industry Lukavac operates with a new catalyst

Polycat MAC 4 ML (manufacturer POLYNT, Italy). The

measured outlet process parameters are: temperature,

pressure, volume percentages of n-butane, carbon dioxide

and carbon monoxide. The best agreement of simulation

results and measured values was achieved with

application of the kinetic model (15) by Schneider et al.

(1987) for December 2015 and February 2016, while the

best agreement for January 2016 was achieved with

application of the kinetic model (16) by Sharma et al.

(1991).

Table 1: Comparisons of results of numerical simulation and

measured values of outlet process parameters.

Kinetic model Tout

(K)

Pout

(bar)

%

n-

butane

%

CO2

%

CO

Measured

value

A 682.74

678.9

683.15

0.664

0.672

0.662

0.29

0.28

0.30

1.05

1.08

1.30

1.03

1.06

1.06

B

C

(7) 650.12 0.644 0.48 3.43 4.58

(8) 657.04 0.622 0.40 3.08 2.01

(9) 640.69 0.659 0.47 2.72 5.42

(10) 650.32 0.644 0.49 3.43 142

(11) 651.97 0.643 0.37 2.89 2.89

(12) 674.09 0.610 0.86 0.91 -

(13) 673.03 0.615 0.43 2.19 -

(14) 628.99 0.689 0.55 4.26 2.12

(15) 682.75 0.601 0.79 0.84 1.21

(16) 678.51 0.603 0.82 0.87 1.05 Legend: A - average values for December 2015, B - average values for

January 2016, C - average values for February 2016, Tout – outlet

temperature of the reaction mixture (K), Pout – outlet pressure of the reaction mixture (bar), %butane - oulet volume percentage of n-butane,

%CO2 - outlet volume percentage of carbon dioxide, %CO - outlet

volume percentage of carbon monoxide.


Table 2: Differences between results of numerical simulation and measured values of outlet process parameters.

The best agreement of simulation values for outlet

pressure and measured values was achieved with

application of the kinetic model (9) by Alonso et al.

(2001). The best agreement of simulation values for

volume percentage of n-butane and measured values was

achieved with application of the kinetic model (11) by

Buchanan and Sundaresan (1986). The best agreement of

simulation values for outlet volume percentage of carbon

dioxide and measured values was achieved with

application of the kinetic model (12) by Centi et al.

(1985).

The best agreement of simulation values for outlet

volume percentage of carbon monoxide and measured

values was achieved with application of the kinetic model

(16) by Sharma et al. (1991). Larger deviations of

simulation values of outlet volume percentages n-butane,

carbon dioxide and carbon monoxide, for some kinetic

models, probably occurred due to the simplified reaction

schemes and simplified reactor model. The largest and the

least deviations for the outlet temperature of reaction

mixture were observed with application of the kinetic

model (14) by Lorences et al. (2003), and with


(1987). The largest and the least deviations for the outlet

pressure of reaction mixture were observed with


(1987) and with application of the kinetic model (9) by

Alonso et al. (2001). The largest and the least deviations

for the outlet volume percent of n-butane were observed

with application of the kinetic model (16) by Sharma et

al. (1991) and with application of the kinetic model (11)

by Buchanan and Sundaresan (1986). The largest and the

least deviations for the outlet volume percent of carbon

dioxide were observed with application of the kinetic

model (14) by Lorences et al. (2003) and with application

of the kinetic model (12) by Centi et al. (1985). The

largest and the least deviations for the outlet percent of

reaction mixture were observed with application of the

kinetic model (9) by Alonso et al. (1991) and with

application of the kinetic model (16) by Sharma et al.

(1991). Table 3: Percentage deviations of results of numerical simulation from

measured values of outlet reactor process parameters.

Figures 1-10 show a comparison of simulated and

measured values for temperatures of the reaction mixture

along reactor length for different kinetic models that were

used in the simulation.

Figure 1: Comparison of simulation and measured values for

temperature of reaction mixture reactor length, in the kinetic model (7) by Alonso et al. (2001

Kinetic

model

Tout

(%)

Pout

(%)

%

n-butane

(%)

%

CO2(%)

% CO

(%)

(7) 4.78

4.24

4.84

3.01

4.17

2.72

-65.52

-71.43

-60.00

-226.67

-217.59

-163.85

-344.66

-332.08

-332.08

(8) 3.76

3.22

3.82

6.33

7.44

6.04

-37.93

-42.86

-33.33

-193.33

-185.19

-136.92

-95.15

-89.62

-89.62

(9) 6.16

5.67

6.21

0.75

1.93

0.45

-62.07

-67.86

-56.67

-159.05

-151.85

-109.23

-426.21

-411.32

-411.32

(10) 4.75

4.21

4.8

3.01

4.17

2.72

-68.97

-75.00

-63.33

-226.67

-217.59

-163.85

-37.86

-33.96

-33.96

(11) 4.51

3.97

4.56

3.16

4.32

2.87

-27.59

-32.14

-23.33

-175.24

-167.59

-122.31

-180.58

-172.64

-172.64

(12) 1.27

0.71

1.33

8.13

9.23

7.85

-196.55

-207.14

-186.67

13.33

15.74

30.00

-

-

-

(13) 1.42

0.86

1.48

7.38

8.48

7.10

-48.28

-53.57

-43.33

-108.57

-102.78

-68.46

-

-

-

(14) 7.87

7.35

7.93

-3.77

-2.53

-4.08

-89.66

-96.43

-83.33

-305.71

-294.44

-227.69

-105.83

-100.00

-100.00

(15) 0.001

-0.57

0.059

9.49

10.57

9.21

-172.41

-182.14

-163.33

20.00

22.22

35.38

-17.48

-14.15

-14.15

(16) 0.62

0.057

0.68

9.19

10.27

8.91

-182.76

-192.86

-173.33

17.14

19.44

33.08

-1.94

0.94

0.94

Kinetic

model

Tout

(K)

Pout

(bar)

%

n-

butane

%

CO2

%

CO

(7) 32.62

28.78

33.03

0.02

0.028

0.018

-0.19

-0.2

-0.18

-2.38

-2.35

-2.13

-3.55

-3.52

-3.52

(8) 25.7

21.86

26.11

0.042

0.05

0.04

-0.11

-0.12

-0.1

-2.03

-2.00

-1.78

-0.98

-0.95

-0.95

(9) 42.05

38.21

42.46

0.005

0.013

0.003

-0.18

-0.19

-0.17

-1.67

-1.64

-1.42

-4.39

-4.36

-4.36

(10) 32.42

28.58

32.83

0.02

0.028

0.018

-0.2

-0.21

-0.19

-2.38

-2.35

-2.13

-0.39

-0.36

-0.36

(11) 30.77

26.93

31.18

0.021

0.029

0.019

-0.08

-0.09

-0.07

-1.84

-1.81

-1.59

-1.86

-1.83

-1.83

(12) 8.65

4.81

9.06

0.054

0.062

0.052

-0.57

-0.58

-0.56

0.14

0.17

0.39

-

-

-

(13) 9.71

5.87

10.12

0.049

0.057

0.047

-0.14

-0.15

-0.13

-1.14

-1.11

-0.89

-

-

-

(14) 53.75

49.91

54.16

-0.025

-0.017

-0.027

-0.26

-0.017

-0.027

-3.21

-3.18

-2.96

-1.09

-1.06

-1.06

(15) -0.01

-3.85

-0.4

0.063

0.071

0.061

0.063

0.071

0.061

0.21

0.24

0.46

-0.18

-0.15

-0.15

(16) 4.23

0.39

4.64

0.061

0.069

0.059

0.061

0.069

0.059

0.18

0.21

0.43

-0.02

0.01

0.01

54 Petric et al.


temperature of reaction mixture along reactor length, in the kinetic

model (8) by Alonso et al. (2001).



model (9) by Alonso et al. (2001).

Alonso et al. (2001) investigated the kinetics (kinetic

models (7), (8) and (9)) in a pilot membrane reactor with

fluid bed of catalyst (located inside the porous

membrane). The application of kinetics obtained from a

fluidized bed reactor on industrial fixed bed reactor is the

main reason for a poor agreement of simulation results

and measured values.



model (10) by Buchanan and Sundaresan (2001).



model (11) by Buchanan and Sundaresan (2001).

Buchanan and Sundaresan (1986) investigated the kinetic

models (10) and (11) and determined the kinetics for

partial oxidation of n-butane over a vanadium phosphate

catalyst. They also determine the effect of phosphorus on

the kinetics of the oxidation of n-butane. They used a

tubular reactor with internal tube diameter of 7 mm and

catalyst diameter of 4 mm. These values significantly

differ from the values in the industrial plant of Global

Ispat Coke Industry Lukavac (internal diameter tubes 21

mm, catalyst diameter 2 mm) and this fact had a major

impact on a poor agreement of simulation results and

measured values in the present study.



model (12) by Centi et al. (1985).

Centi et al. (1985) investigated the kinetic model (12)

and they used a fixed bed reactor based on a vanadium-

phosphorus oxide. Inlet temperature was between 370 and

410°C. Simulated temperatures of the reaction mixture

along reactor length with kinetic model (6) show good

agreement with measured values in the present study, due

to the use of similar type of reactor and the same type of

catalyst. The kinetic model (14) uses the concentrations

of n-butane, oxygen, and maleic acid.




model (13) by Marín et al. (2010).

Marín et al. (2010) investigated the kinetic model for


membrane reactor with enhanced heat transfer through

the membrane walls. They also investigated the influence

of the reactor length, flow rate of gas phase, inlet

temperature of reaction mixture and inlet concentration of

n-butane on n-butane conversion and selectivity of maleic

anhydride. They used fixed bed reactor with the catalyst

tubes (inner diameter of 34 mm, length of 0.5 m). In the

industrial plant of Global Ispat Coke Industry Lukavac, a

reactor tube with inner diameter 21 mm and length of 3.7

m was used. Therefore, the differences in the main

dimensions of reactor (laboratory reactor versus industrial

reactor) were possible cause of a poor agreement of

simulation results and measured values in the present

study. The kinetic model (13) uses the concentration of n-

butane, oxygen, and maleic acid.



model (14) by Lorences et al. (2003).

Lorences et al. (2003) investigated the kinetic model (14)

with a wide range of operating conditions in order to

assess their impacts on environmental pollution, the

selectivity of maleic anhydride, bio-based products,

productivity and reaction rate. The experiment was

performed with a catalyst based on a vanadium-

phosphorus oxide in a fluid bed reactor (inner diameter

0.04 m, height of 0.79 m). Volume percentages of n-

butane at reactor inlet were 2, 5 and 9%. In the industrial

reactor of Global Ispat Coke Industry Lukavac, volume

percentage of n-butane at reactor inlet is 1.65%. The

differences in the inlet volume percentages of n-butane is

probably the main reason for a poor agreement of

simulation and measured values in the present study.

Moreover, kinetic model (14) uses the concentrations of

n-butane, oxygen, and maleic acid.



model (15) by Schneider et al. (1987).

Schneider et al. (1987) investigated the kinetics (kinetic

model (15)) of the oxidation of n-butane over a catalyst

based on a vanadium-phosphorus oxide (reactor length of

6.5 m, internal diameter of tube 1.15 cm). Simulation

values of temperatures of the reaction mixture along

reactor length for the kinetic model (15) show a good

agreement with measured values (Figure 9) in the present

study. The kinetic model (15) uses the partial pressures of

n-butane, oxygen, and maleic acid.



model (16) by Sharma et al. (1991).

Sharma et al. (1991) investigated the kinetic model of

selective oxidation of n-butane to maleic anhydride

(kinetic model (16)). They used data from commercial

fixed bed reactor with a catalyst based on a vanadium-

phosphorus oxide. The volume percentage of butane at

the reactor was 1.81%. The kinetic model used the partial

pressures of n-butane, oxygen, and maleic acid.

Simulation values of temperature of reaction mixture

along reactor length show good agreement with measured

values (Figure 10) in the present study. The same type of

reactor and similar type of catalyst was used in industrial

plant of Global Ispat Coke Industry Lukavac. Figures 11-

16 show the conversion of n-butane, the yield of maleic

anhydride, the selectivity of maleic anhydride, the molar

flow rate of n-butane, the molar flow rate of maleic

anhydride, the molar flow rate of oxygen, all along

reactor length (the application of kinetic model by

Sharma et al. (1991)).

56 Petric et al.

Figure 11: Conversion of n-butane along reactor length.

Centi et al. (1985) investigated the dependence of the

conversion of n-butane as a function of space time from

the physical inputs of the reaction mixture in the reactor

to the exit of the reaction mixture from the reactor. Space

time is correspondent with length of the reactor tube. The

conversion of n-butane is increased by the space-time

reactor, as shown in the present study.

Figure 12: Yield of maleic anhydride along reactor length.

Alonso et al. (2001) investigated the yield of maleic

anhydride by the length of the reactor tube. They found

that the yield of maleic anhydride along reactor length,

which has been shown in this study.

Figure 13: Selectivity of maleic anhydride along reactor length.

Moser and Schrader (1984) investigated the selectivity of

maleic anhydride in a function of space time from the

inlet of reaction mixture in a reactor to the outlet of

reaction mixture from the reactor. The selectivity of

maleic anhydride was increased with increase of space

time, as it was shown in the present study.

Figure 14: Molar flow rate of n-butane along reactor length.

Figure 15: Molar flow rate of maleic anhydride along reactor

length.

Figure 16: Molar flow rate of oxygen along reactor length.

CONCLUSIONS

The mathematical model for numerical simulation of


fixed bed reactor was developed. Validation of developed

mathematical model was performed with real process data

from industrial reactor. The mathematical model was

validated with three process data sets of five measured

variables (temperature, pressure, concentration of n-

butane, concentration of carbon dioxide, concentration of

carbon monoxide) and with application of ten kinetic

models from literature. Comparison of simulation results

and measured data showed a good agreement with

application of three kinetic models: (12) (by Centi et al.,

1985), (15) (by Schneider et al., 1987) and (16) (by

Sharma et al., 1991). The profiles along reactor length for

the main process parameters (conversion of n-butane, the

yield of maleic anhydride, the selectivity of maleic


anhydride, the molar flow rate of n-butane, the molar

flow rate of maleic anhydride, the molar flow rate of

oxygen) were also analyzed. Future work is directed to

improvement of the model, further validation of the

model and optimization of the process performance.

REFERENCES

Alonso, M., Lorences, M.J., Pina, M.P., Patience, G.S.

(2001). Butane partial oxidation in an externally

fluidized bed-membrane reactor. Catalysis Today,

67(1-3), 151-157.

Buchanan, J.S., Sundaresan, S. (1986). Kinetics and

Redox Properties of Vanadium Phosphate

Catalysts for Butane Oxidation. Applied Catalysis,

26, 211-226.

Centi, G., Fornasari, G., Trifirò, F. (1985). n-butane

oxidation to maleic anhydride on vanadium-

phospohorus oxides: Kinetic analysis with a

tubular flow stacked-pellet reactor. Industrial &

Engineering Chemistry Research Development,

24, 32-37.

Cruz-Lopez, A., Guilhaume, N., Miachon, S., Dalmon,

J.A. (2005). Selective oxidation of butane to maleic

anhydride in a catalytic membrane reactor adapted

to rich butane feed. Catalysis Today, 107-108, 949-

956.

Dente, M., Pierucci, S., Tronconi, E., Cecchini, Mr.,

Ghelfi, F. (2003). Selective oxidation of n-butane to

maleic anhydride in fluid bed reactors: detailed

kinetic investigation and reactor modelling.

Chemical Engineering Science, 58, 643-648.

Diedenhoven, J., Reitzmann, A., Mestl, G., Turek, T.

(2012). A Model for the Phosphorus Dynamics of

VPO Catalysts during the Selective Oxidation of n-

Butane to Maleic Anhydride in a Tubular Reactor.

Chemie Ingenieur Technik, 84(4), 1-8.

Gascón, J., Valenciano, R., Téllez, C., Herguido, J.,

Menéndez, M. (2006). A generalized kinetic model

for the partial oxidation of n-butane to maleic

anhydride under aerobic and anaerobic conditions.


Lorences, M.J., Patience, G.S., Dìez, F.V., Coca, J.

(2003). Butane Oxidation to Maleic Anhydride:

Kinetic Modeling and Byproducts. Industrial &

Engineering Chemistry Research, 42, 6730-6742.

Marín, P., Hamel, C., Ordóñez, S., Dìez, F.V., Tsotsas,

E., Seidel-Morgenstern, A. (2010). Analysis of a

fluidized bed membrane reactor for butane partial

oxidation to maleic anhydride: 2D modelling.


Moser, T.P., Schrader, G.L., 1985. Selective Oxidation of

n-Butane to Maleic Anhydride by Model V-P-O

Catalysis. Journal of Catalysis, 92, 216-231.

Schneider, P., Emig, G., Hofmann, H. (1987). Kinetic

investigation and reactor simulation for the catalytic

gas-phase oxidation of n-butane to maleic

anhydride. Industrial & Engineering Chemistry

Research, 26, 2326-2241.

Sharma, R.K., Cresswell, D.L., Newson, E.J. (1991).

Kinetics and fixed-bed reactor modelling of butane

oxidation to maleic anhydride. American Institute of

Chemical Engineers Journal, 37(1), 39-47.

Trifirò, F., Grasselli, R. K. (2014). How the Yield of

Maleic Anhydride in n-Butane Oxidation, Using

VPO Catalysts, was Improved Over the

Years. Topics in Catalysis, 57(14-16), 1188-1195.

58 Petric et al.

Summary/Sažetak

Ciljevi ove studije su bili: razvoj matematičkog modela za numeričku simulaciju oksidacije n-butana u anhidrid

maleinske kiseline u industrijskom cijevnom reaktoru sa nepokretnim slojem katalizatora i verifikacija

razvijenog matematičkog modela sa stvarnim procesnim veličinama sa industrijskog reaktora koji se nalazi

Global Ispat Koksna Industrija d.o.o. Lukavac. Matematički model se sastoji od diferencijalnih jednačina koje

opisuju materijalni bilans svake komponente, energetski bilans, stehiometriju reakcija, pad pritiska, kinetički

model. Za rješavanje diferencijalnih jednačina korišten je numerički softverski paket Polymath sa Runge-Kutta-

Fehlberg metodom. Razvijeni matematički model je verificiran sa tri seta procesnih podataka od pet mjerenih

varijabli (temperatura, pritisak, koncentracija n-butana, koncentracija ugljikovog dioksida, koncentracija

ugljikovog monoksida) i sa primjenom deset kinetičkih modela preuzetih iz literature. Usporedba simulacijskih

rezultata i mjerenih podataka je pokazala dobro slaganje za tri kinetička modela. Za odabrani kinetički model,

prikazani su profili temperature, molarnih protoka, konverzije n-butana i selektivnosti anhidrida maleinske

kiseline.

Nanosensors applications in agriculture and food industry

Enisa Omanović-Mikličanina, Mirjana Maksimović

b

aFaculty of Agriculture and Food Sciences, University of Sarajevo, Zmaja od Bosne 8, 71 000 Sarajevo, B&H

bFaculty of Electrical Engineering, University of East Sarajevo, Vuka Karadžića 30, 71123 East Sarajevo, B&H

INTRODUCTION

Food safety is a prime concern of human life. With

increased globalization of food this implies the

importance of food quality assessment in all steps of agri-

food supply chain. The chain includes all steps “from the

farm to the table” production, distribution, processing,

and marketing of agricultural food products to the final

consumers (Fig. 1) (Ahumada and Villalobos 2009).

Abstract info Received: 25/10/2016 Accepted: 21/12/2016

Keywords:

Nanotechnology

Nanosensors Agriculture

Food

Food Industry

Corresponding author:

Enisa Omanović-Mikličanin

[email protected]

+387 33 225 727

+387 33 667 427

Tel:

Fax:

Abstract: Food safety is very important issue in food industry and agriculture

because it is directly related to the influence of food on the human health. Recent

food safety incidents (such as the melamine affair in 2007 and 2008) and public

health concerns about synthetic food additives and chemical residues in food have

driven the need to develop rapid, sensitive, and reliable methods to detect those

food hazards. An alternative is given in the rapid development of nanosensors

which have advantage to detect food components in an easy and quick manner.

Linking nanosensors with modern Information and Communication Technologies

(ICTs) enables novel and online ways for different components detection

accompanied with high accuracy. Various types of nanosensors are being developed

to meet the different requirements in food inspection (nanosensors for detection of

external and internal conditions in food packaging, carbon nanotubes based

electrochemical sensors for detection of cations, anions and organic compounds in

food, various aptamers for detection of pesticides, antibiotics, heavy metals,

microbial cells and toxins).

The work reviews development and application of the most present nanosensors in

agriculture and food industry.




2016

47

59-70 UDC: __________________________

Technical Paper

60 Omanović-Mikličanin & Maksimović

Fig.1 Agri-food supply chain (QS Article 2012)

Globalization of food production along with consumer

concerns related to food quality and safety have resulted

in interconnected and global systems for the production

and distribution of food, followed by significant increase

in food standards (Sharma et al. 2015). This approach

required a move from the former end-of-line product

inspection approach to a new environment in which

quality assurance is required at every step of food

production chain to ensure safe food and to show

compliance with regulatory and customer requirements.

As a result, upcoming quality and food safety assessment

procedures will require additional essential elements such

as low detection limits, high sensitivity and specificity,

miniaturization of instrumentation for portable use,

simple sample preparation steps (Craig et al. 2013).

At laboratory level, food safety can be quantitatively

assessed by various methods, including cell culture and

fine instrumental analysis. The main disadvantage of

these methods is long analysis time ranging from several

hours to days, usually with different pretreatment steps.

However, food safety is nowadays threatened by

contaminants which cannot be detected with standard

analytical methods e.g. formaldehyde, plant pathogens,

excessive pesticide residues, (Li and Sheng 2014). The

inadequacy of conventional methods to solve new

challenges in food safety, leads to the development of

new, miniaturized and fast analytical techniques with low

detection limits. Nanotechnology aspects integrated with

analytical tools present one of the key solutions for

development of new devices.

In addition, nanotechnology, as one of the exciting new

fields of research, has great promise in addressing many

of the pressing needs in the food and agriculture sectors

by opening new avenues of food production,

manufacturing and packaging, plant cropping and animal

feeding. In the food sector, this technology has great

potential to improve food functionality and quality.

Unfortunately, many of the nanotechnological tools for

food and agriculture field are still on a research level.

Only practical applications of nanotechnology are in food

packaging and in nanosensors for detection of food

contaminants. Nanotechnology, nanominerals and

nanosensors in the agri-food sector, including feed and

nutrient components, intelligent packaging and quick-

detection systems, can be seen as new source of key

improvements in the agrarian sector (Fig. 2).


Fig.2 Nanotechnology’s potential benefits to many areas of the food industry (Duncan, 2011)

Innovative enhancements for the molecular treatment of

diseases, rapid disease detection, successful dealing with

viruses and crop pathogens, enhancing the power of

plants to absorb nutrients as well as lowering usage doses

of pesticides and herbicides proved that nanotechnology

has the potential to revolutionize the agricultural and food

industry (Joseph and Morrison 2006). To understand and

address the importance of nanotechnology in the agri-

food sector, this paper represents a review of types and

applications of the most present nanosensors in

agriculture and food industry. Even there is a belief that

nanotechnology will also protect the environment

indirectly through the use of renewable energy supplies,

and filters or catalysts to reduce pollution and clean-up

existing pollutants (Joseph and Morrison 2006), there are

still many concerns regarding nanomaterials toxicity and

possible negative impact on the surroundings. Alongside

the potential advantages of nanotechnology applications

in the agri-food supply chain, concerns regarding

nanotoxicology and safety will be discussed as well.

Nanosensors

Nanosensors are emerging as a promising tools for the

applications in the agriculture and food production. They

offer significant improvements in selectivity, speed and

sensitivity compared to traditional chemical and

biological methods. Nanosensors can be used for

determination of microbes, contaminants, pollutants and

food freshness (Joyner and Kumar 2015).

The nanosensors used in food analyses combine

knowledge of biology, chemistry and nanotechnology and

may also be called nanobiosensors.

Need for nanosensors

The part of food production where the need for the

nanosensors is the most visible is food packaging and

food transport.

Food packaging prevent sensory exposure from the foods

thus consumers must rely on expiry dates provided by

producers based on a set of idealized assumptions about

the way that the food is stored or transported. If the

transport or storage conditions are violated for any period

of time, the quality of food might be deteriorated which

might not be known to the consumer unless the food

package is opened, or even consumed (Joyner and Kumar

2015).

Nanosensors can improve the disadvantages of food

packaging through their unique chemical and electro-

optical properties. They are able to detect the presence of

gasses, aromas, chemical contaminants, pathogens, and

even changes in environmental conditions. Nanosensors

ensure that consumers purchase fresh and tasty products

and reduce the frequency of food-borne illnesses which

improve food safety.

Development of nanosensors in agri-food sector

Nanosensors have the arrangement like ordinary sensors,

but their production is at the nanoscale. Therefore,

nanosensor can be defined as an extremely small device

than can bind to whatever is wanted to be detected and

send back a signal. These tiny sensors are capable of

detecting and responding to physicochemical (sensors)

and biological signal (biosensors), transferring that

response into a signal or output that can be used by

humans.


Compared with traditional sensors and their

shortcomings, nanosensors have several advantageous

properties, such as high sensitivity and selectivity, near

real-time detection, low cost and portability and other

necessary attributes which are improved by using

nanomaterials in their construction (Lu and Bowles

2013). There are many techniques for development of

nanosensors which involves top-down lithography,

molecular self-assembly and bottom-up assembly

approaches. Current nanosensors devices can be divided

into (Liu 2003):

Nanostructured materials - e.g. porous silicon,

Nanoparticles based sensors,

Nanoprobes,

Nanowire nanosensors,

Nanosystems: cantilevers, Nano-electromechanical

systems (NEMS).

Based on applications in food analysis, nanosensors can

be divided on:

Nanoparticle based nanosensors

Electrochemical nanosensors

Optical nanosensors

Aptasensors

Aptasensors are biosensors consisting of aptamers (the

target-recognition element) and nanomaterial (the signal

transducers and/or signal enhancers). Aptamers are single

stranded nucleic acid or peptide molecules of size less

than 25 kDa with natural or synthetic origin. They are

highly specific and selective towards their target

compound (ions, proteins, toxins, microbes, viruses) due

to their precise and well defined three-dimensional

structures. Aptamers are named as synthetic antibodies

due to their selection and generation through an in vitro

combinatorial molecular technique called SELEX.

Dissociation constants of aptamers are in nanomolar or

picomolar range. Aptamers are extensively used as

recognition elements in the fabrication of aptasensors

(Sharma et al. 2015). There are a wide variety of

nanomaterials, which can be used in aptasensors (metal

nanoparticles and nanoclusters, semiconductor

nanoparticles, carbon nanoparticles, magnetic

nanoparticles etc) (Sharma et al. 2015). Also, a wide

variety of transducing systems have been employed in

aptasensors for food quality assessment and safety. The

principles of aptasensors are based on the property of the

nanoparticle being used. Based on the detection systems,

aptamers can be classified into optical and

electrochemical systems.

Nanosensors applications

Numerous nanosensors are developed for various

applications in agricultural and food industry either to

quickly identify threats in the case of suspected food

poisoning, or integrated into packaging as nanotracers to

show the history of the food product and whether it is of

acceptable quality at any given time. For instance, the use

of nanosensors in food packaging to detect growth of

microorganisms and change color when a threshold level

is reached, as well as nanosensors applied in on-line

process control, for monitoring of storage conditions are

useful for preventing food poisoning (Augustin and

Sanguansri 2009). As another example, scientists are

making the gold nanoparticles and coat them with

molecules that can bind to substances like pesticides.

Farmers could spray these nanoparticles on their fields to

detect a chemical like a pesticide (Rathbun 2013).

Furthermore, nanosensors employing Raman

spectroscopy are ideally suited for food forensic. Food

forensics is investigation of food origin, adulteration and

contamination. Nanosensors application in this

contributes to the specificity of the method and allows

application of various analytes which can be probed;

ranging from the macro-food, lipids, proteins and

carbohydrates, to the minor components, dyes, pigments,

preservatives.

More examples of nanosensors and their development for

agriculture and food applications are depicted in

(Augustin and Sanguansri 2009; Rai et al. 2012; Parisi et

al. 2014). According to Fig. 2 and performed surveys of

research articles (Joseph and Morrison 2006; Valdes et al.

2009; Lu and Bowles 2013; Prasad et al. 2014; Nanowerk

2014; Sekhon 2014; Berekaa 2015; Rai et al. 2015), the

list of potential applications of nanosensors in agri-food

supply chain can be summarized, as it is shown in Table

1.

It can be highlighted that nanosensors are useful for

sensing and reporting real time information regarding the

product from production through to delivery to the

consumer. Nanosensors are far from being simply a

passive, information-receiving device. They can get

information from immediate and remote contexts and can

analyze, record and report data. They can be designed to

manage this at critical control points in the supply chain -

from the point food is produced or packaged, through to

the time it is consumed.

The latest developments have resulted innanosensors

which are quite near commercialization: nanosensors and

nanoscale coatings to replace thicker, more wasteful

polymer coatings for preventing corrosion, nanosensors

for detection of aquatic toxins, nanoscale biopolymers for

improved decontamination and recycling of heavy

metals, nanosensors able to provide quality assurance by

tracking microbes, toxins and contaminants through the

food processing chain by using data capture for automatic

control functions and documentation, among others

(Prasad et al 2014; Lu and Bowles 2013).

Gold nanoparticles functionalized with cyanuric acid

groups selectively bind to melamine, an adulterant used to

artificially inflate the measured proteins content of pet

foods and infant formulas (Ai et. al, 2009).

A promising photoactivated indicator ink for in-package

oxygen detection based upon nanosized TiO2 or SnO2

particles and a redox-dye (methylene blue) has been

developed (Mills, 2005) (Fig. 3).

Nanosensors based on nanoparticles have also been

developed to detect the presence of moisture content

inside a food packaging (Luechinger et al., 2007) (Fig. 4).

Fig.3 Photographs of O2 sensors which utilize UV-activated TiO2 nanoparticles and methylene blue indicator dye, one placed inside of a food

package flushed with CO2 and one placed outside. In (a) the package is freshly sealed and both indicators are blue. The photograph in (b) shows

the indicators immediately after activation with UVA light. After a few minutes, the indicator outside of the package returns to a blue color,

whereas the indicator in an oxygen-free atmosphere remains white (c) until the package is opened, in which case the influx of oxygen causes it to

change back to blue (d). This system could be used to easily and noninvasively detect the presence of leaks in every package immediately after

production and at retail sites (Adapted from: Mills, 2005)

Fig.4 Moisture sensor which utilizes carbon-coated copper nanoparticles dispersed in a polymer matrix (a). Ethanol vapor exposure results in

rapid and reversible iridescent coloration (b). Water vapor exposure swells the polymer, which causes the nanoparticles to exhibit larger

interparticle separation distances and thus different observable optical behavior (c). As moisture dissipates (d–f), the sensor reverts back to its

native state and appearance (Adapted from: Luechinger et al., 2007)


Table 1: Nanosensors potential applications in agri-food sector

Agriculture Food processing Food packaging Food transport Nutrition

Nanosensors for

monitoring soil

conditions (e.g.

moisture, soil pH), a

wide variety of

pesticides, herbicides,

fertilizers, insecticides,

pathogens and crop

growth as well

Nanosensors for

detection of food-borne

contaminants or for

monitoring

environmental

conditions at the farm

Nanochips for identity

preservation and

tracking

Nanocapsules for

delivery of pesticides,

herbicides, fertilizers

and vaccines

Nanosensors and nano-

based smart delivery

systems for efficient use

of agricultural natural

resources (e.g. water),

nutrients and chemicals

through precision

farming

Nanoparticles to deliver

growth hormones or

DNA to plants in

controlled manner

Nanoparticles used as

smart nanosensors for

early warning of

changing conditions that

are able to respond to

different conditions

Aptasensors for

determination of

pesticides and

insecticides (e.g.

phorate, acetamiprid,

isocarbophos)

Aptasensors for

determination of

antibiotics, drugs and

their residues (e.g.

cocaine,

oxytetracycline,

tetracycline,

kanamycin).

Aptasensors for

determination of heavy

metals (e.g. Hg2+, As3+,

Cu2+)

Nanoencapsulated

flavor enhancers

Nanotubes and

nanoparticles as

gelation and

viscosifying agents

Nanocapsule

infusion of plant

based steroids to

replace a meat’s

cholesterol

Nanoparticles to

selectively bind and

remove chemicals or

pathogens from food

Aptasensors for

determination of

microbial toxins

(e.g. OTA,

Fumonisin B1)

Portable nanosensors to

detect chemicals,

pathogens and toxins in

food

DNA biochips to detect

pathogens and to

determine the presence of

different kind of harmful

bacteria in meat or fish,

or fungi affecting fruit

Nanosensors incorporated

into packaging materials

for detection of chemicals

released during food

spoilage and serve as

electronic tongue (e.g.

bitter, sweet, salty,

umami, and sour

detection), or nose (e.g.

wine characterization)

Electromechanical

nanosensors to detect

ethylene

Nanosensors applied as

labels or coating to add

an intelligent function to

food packaging in terms

of ensuring the integrity

of the package through

detection of leaks,

indication of time-

temperature variations

and microbial safety

Aptasensors for

determination of

microbial cells (e.g.

Salmonella typhimurium,

Escherichia Coli, Listeria

monocytogenes)

Aptasensors for

determination of

antibiotics, drugs and

their residues (e.g.

cocaine, oxytetracycline,

tetracycline, kanamycin).

Aptasensors for

determination of heavy

metals (e.g. Hg2+, As3+,

Cu2+)

Nanosensors for

monitoring

environmental

conditions during

distribution and

storage

Nanosensors for

traceability and

monitoring

product conditions

during transport

and storage, what

is crucial for

products which

have a limited

shelf-life

Smart-sensor

technology for

monitoring the

quality of grain,

dairy products,

fruit and

vegetables in a

storage

environment in

order to detect the

source and the

type of spoilage

Aptasensors for

determination of

microbial cells

(e.g. Salmonella

typhimurium,

Escherichia Coli,

Listeria

monocytogenes)

Nanocapsules

incorporated into

food to deliver

nutrients

Nanocochleates (50

nm coiled

nanoparticles) for

delivering nutrients

(e.g. vitamins,

lycopene, and omega

fatty acids) more

efficiently to cells,

without affecting the

color or taste of food


Toxicology and safety aspects of nanosensors

utilization in agricultural and food industry

Despite the tremendous benefits of nanosensors in the

agriculture and food industry, there is a huge public

concern regarding toxicity and environmental effect.

There is very limited knowledge about its long term

adverse effect on soil, plants and ultimately on human

(Dubey and Mailapalli 2016). Preliminary studies on

animal have shown potential toxicity of nanomaterials for

liver, kidneys, and immune system. Additionally, the

effects of exposure to engineered nanoparticles may be

dissimilar from the effects induced by naturally occurring

nanoparticles (Rai and Ingle 2012). Therefore, risk

assessment studies to show adverse effects of

nanoparticles on human health and the environment

should be standardized and their number should be

increased while at the same time the field of

nanotoxicology emerges to international level. Obviously,

the necessity of awareness of the nanoparticles presence

in the environment and their detection, the measurement

of nanoparticles emissions, life-cycle, toxicity and impact

to human health and environment are crucial in achieving

all the benefits nanotechnologies has to offer.

The toxicity of nanoparticles in the environment depends

on their size, type, charge, etc. Additionally, the influence

of nanoparticles on the environment depends also of the

environmental factors (humidity, temperature, wind flow

rate, the nature of light, etc). However, properties of

nanomaterials, small size and large surface allow easy

dispersion and bonding in the environment and with

human tissues.

One way in which nanomaterials enter the environment

and humans is through agriculture sector (Rico 2015).

Nanoparticles strongly interact with soils. Nanomaterials

enhanced plant foods and pesticides are able to disperse

into soil, water and atmosphere, to bond more strongly

with pollutants and carry them through soil and water

(Sastry 2012). Exposure to nanofertilizers and pesticides,

can contribute to health hazards (Sastry 2012). Migration

of nanoparticle incorporated in food material to human is

also high risk (Berekaa 2015). In other words, direct

exposure of consumers to nanomaterials poses a serious

problem to human health. Nanoparticles can enter the

human body through the skin, respiratory system or

digestive system (Fig. 5). However, as long as the

nanoparticles remain bound, exposure is limited or very

low. Health impact and safety regarding the application of

nanomaterials was reported in detail by Teow et. al

(2011).

Nanoparticles

BrainNeurological

diseases

Circulatory system

Lungs

Asthma, Bronchitis, Cancer

Heart, other organs and lympatis system

diseases

Skin Inhalation IngestionAutoimmune

deseases, dermatitis

Chron’s disease, Colon cancer

Fig. 5. Potential harmful effects of nanoparticles to human health

Modern techniques revealed that nanomaterials with

higher reactivity and ability to cross membrane barriers

can lead to different toxico-kinetic and toxico-dynamic

properties. Some nanomaterials interact with proteins and

enzymes leading to oxidative stress and generation of

ROS, which cause destruction of mitochondria and

produce apoptosis.

Over the last few years there have been numerous

publications reporting a variety of biological and

toxicological interactions of nanomaterials in in vitro and

in vivo experimental systems. A wide range of

biochemical and toxicological endpoints within each

system have been reported. Most have been directed to

proinflammatory and inflammatory markers since

existing knowledge on the health effects of ambient fine

particulates and nanomaterials has identified a central role

for oxidative stress and inflammation in the toxicological

mode of action of nanoparticles.

Therefore, the understanding of the biological and

toxicological effects of nanomaterials has significantly

advanced in the last few years. Much of this has been in

relation to what type of physical characterization and

toxicological data is required for hazard and risk

assessment, and how to go about obtaining it. Serious

adverse effects have not been observed in limited

applications to nanomaterials of “traditional” tests for

assessing the acute toxicity of chemicals. The

toxicological data sets available for nanomaterials remain

rudimentary, for example long term inhalation studies,

reproductive or developmental studies are not available.

The fact that, if nanoparticles are absorbed into the

systemic circulation, they may be retained within cells for

long periods, makes it imperative that chronic studies be

undertaken for hazard and risk assessment of

nanomaterials.

One of the potential solutions to make nanotechnology

applications as safe as possible is moving towards green

nanotechnology. Safe and energy efficient nanomaterials,

nanoproducts and processes, reduced waste, lessen

greenhouse gas emissions and usages of renewable

materials are the benefits green nanotechnology concept

offers. Even this concept is still in the lab/start-up phase,

it is anticipated that by greening nanotechnology,

environmental, health and societal benefits of

nanoparticle production and nanoparticle application in

various fields will be maximized (Dahl et al. 2007, OECD

2013, Goel and Bhatnagar 2014).


Nanosensors in the realization of smart agri-food

sector

Nanotechnology is recognized by the European

Commission as one of its six “Key Enabling

Technologies” that have potential to significantly

improve various industrial sectors. Even nanotechnology

application to the agriculture and food sectors is at the

nascent stage compared with some other sectors, like drug

delivery and pharmaceuticals, the current challenges of

sustainability, food security and climate change implicate

enhanced researchers' interest in exploring the role and

significance of nanotechnology in the improvement

processes of the agrarian sector (Parisi et al. 2015).

Nanotechnology holds the potential to revolutionize the

global food system, and there are a lot more applications

in agriculture and food system that can be expected in the

years to come (Prasad et al. 2014). Novel agricultural and

food safety systems, disease-treatment delivery methods,

tools for molecular and cellular biology, sensors for

detecting pathogens and pesticides, intelligent packaging

materials, environmental protection, and education of the

public and future workforce are some of examples of the

important impact that nanotechnology could have in

agrarian sector (Garcia et al. 2010).

Inexpensive sensors, cloud computing and intelligent

software together can revolutionize the whole agri-food

sector. Thanks to the computers, global satellite

positioning systems and remote sensing devices, precise

farming becomes reality. Monitoring and measuring of

environmental variables, and making appropriate and on-

time decisions and performing targeted action according

to collected data in order to maximize output (i.e. crop

yields) with optimal use of resources (i.e. fertilizers,

pesticides, herbicides, etc.) are the main characteristics of

precise farming vision (Rai and Ingle 2012).

It is important to highlight various micro-nano bio

systems, developed as projects of European Commission

(2015) and used to realize smart agri-food systems.

Technology development, particularly Internet of Things

(IoT), as an emerging reality where more and more

devices are connected to users and other devices via the

Internet, significantly contribute to the realization of the

smart agriculture by increasing the quality, quantity,

sustainability and cost effectiveness of agricultural

production. The innovative application of nanotechnology

in IoT creates a new paradigm, namely the Internet of

Nano Things (IoNT). Nanosensors, because of their small

dimensions, can collect information from numerous

different points. Nanosensors made from non-biological

materials, such as carbon nanotubes, have ability to sense

and signal, acting as wireless nanoantennas (Garcia-

Martinez 2016).

External devices can then integrate the data to

automatically generate incredibly detailed report and

respond to potentially devastating changes in their

environment. There are numerous intelligent

nanosystems, used in the smart agriculture for the crop

monitoring, immobilization of nutrients and their release

in soil, analysis of pesticides, moisture and soil pH.

Networks of connected nanosensors for monitoring soil or

plant conditions have ability to alert automatically

according to conditions detected by sensors and therefore

influence more efficient usage of the water, fertilizers,

herbicide, pesticide, insecticide, etc. In this way, a real

time and comprehensive monitoring of the crop growth,

lead to accurate and on-time decisions, reduced costs and

waste, improved quality of production and above all, to

sustainable agriculture.

As food safety is a main concern nowadays, it is essential

to have manners and systems which enable foodstuffs

traceability and monitoring during the whole food chain

process (Maksimovic et al. 2015). Nanosensors can be

applied along the entire food supply chain, in order to

detect different targets in quickly, sensitive, and cost-

effective manners. This is necessary in the process of

ensuring food quality, safety, freshness, authenticity, and

traceability (Fraceto et al. 2016). For instance, involving

nanosensors in the design of smart or intelligent

packaging, spoilage or harmful contaminants during

distribution or storage can be detected alongside enabled

transfer of information regarding product conditions. In

this way food producers, transportation and

hospitality/retail companies become connected as never

before.

The response generated due to changes related to internal

or external environmental factor, are recorded through

specific sensors (Berekaa 2015), stored in the database

and via Internet 24/7 available insight into soil and crop

health, food contaminations, quality, etc (Fig. 6). In this

way, rapid response and reactions to detections of

abnormally parameters’ values, are enabled and lead to

more quality and safe food, what direct influence to

human health. However, to achieve the full potential

IoNT has to offer in agriculture and food industry, many

concerns, regarding data security and privacy as well as

nanomaterial’s toxicity and impact to the environment

and human must be considered.


Fig.6 IoT architecture for the agri-food sector (Gannon 2016)

CONCLUSION

Recent advancements in nanotechnology, embraced with

intense research at both academic and industrial levels

and advancements in ICTs, show its potential to

positively influence the agri-food sector. Improved

quality of the soil, increased productivity, stimulation of

plant growth, the use of precise farming, monitoring food

quality and freshness during production, processing,

distribution and storage, are just some of the many

benefits nanotechnology, nanomaterials and nanosensors

have to offer in agricultural and food industry. The

excellent specificity of the nanosensors and aptamers

allows an analysis of wide variety analytes, including

heavy metal ions, toxins, pathogens, small molecules,

nucleic acids and proteins. Nanoparticles add on to the

selectivity and convenience of the diagnostics, by the

providing larger surface area for aptamer immobilization

as well as by conferring their own opto-physical and

electrochemical properties to the sensor. Some obstacles

still exist in the development of field-applicable

nanosensor techniques (sample pretreatment technique,

specificity, expenses). It can be hoped that further insight

into the probable solutions to these problems and in the

development of novel nanomaterials will boost designing

of affordable and easily operable nanomaterial based

sensing system. However, the full potential of

nanotechnology in agriculture and food sector is yet to be

realized and can be achieved only with improved

awareness and knowledge of the potential harm from

nano-enabled products, and the long-term impacts of

nanomaterials to the environment and human health. The

future researches will be focused in the development of

novel reliable material, methods and smart devices on the

nano-scale, to the realization of IoNT vision, as well as to

the evaluation of their impact on the human and

environment. Moving towards green nanotechnology and

green IoT will lead to a whole new world of safe

nanoproducts and their widespread applications with little

or no hazards to human health and the environment.

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Summary/Sažetak

Sigurnost hrane je vrlo važan aspekt u prehrambenoj industriji i poljoprivredi, jer je u direktnoj vezi sa uticajem hrane na

zdravlje ljudi. Nedavni incidenti povezani sa sigurnošću hrane (kao što su afere melamina u 2007. i 2008.) i zabrinutost

javnosti zbog sintetičkih aditiva i hemijskih ostataka u hrani istakli su važnost razvoja brzih, osjetljivih i pouzdanih metoda

za otkrivanje kontaminanata u hrani. Značajan doprinos sigurnosti hrane daju nanosenzori koji određuju komponente i

kontaminante hrane na brz i jednostavan način i u malim koncentracijama. Povezivanje nanosenzora sa modernim

informacijskim i komunikacijskim tehnologijama (IKT) omogućava nove i online načine za detekciju različitih

komponenti uz visok procenat tačnosti. Postoje različite vrste nanosenzora koji odgovaraju na zahtjeve u inspekciji hrane

(nanosenzori integrisani u pakovanju za detekciju vanjskih i unutrašnjih parametara, elektrohemijski senzori na bazi

ugljeničnih nanocijevi za detekciju kationa, aniona i organskih spojeva u hrani, razni aptameri za detekciju pesticida,

antibiotika, teških metala, ćelijskih mikroba i toksina). Ovaj rad predstavlja pregled razvoja i aplikacija najprisutnijih

nanosenzora u poljoprivredi i prehrambenoj industriji.


71 Bulletin of the Chemists and Technologists of Bosnia and Herzegovina

INSTRUCTIONS FOR AUTHORS

GENERAL INFORMATION

Bulletin of the Chemists and Technologists of Bosnia and Herzegovina (Glasnik hemičara i tehnologa Bosne i Hercegovine) is a semiannual international journal publishing papers from all fields of chemistry and related disciplines. Categories of Contributions

1. Original Scientific Papers – (about 10 typewritten pages) report original research which has not been published previously, except in a preliminary form. The paper should contain all the necessary information to enable reproducibility of the described work.

2. Short Communications – (about 5 typewritten pages) describing work that may be of a preliminary nature but which merits immediate publication.

3. Notes – (about 3 typewritten pages) report unpublished results of short, but complete, original research or describe original laboratory techniques.

4. Reviews – (about 30 typewritten pages) present a concise and critical survey of a specific research area. Generally, these are prepared by the invitation of the Editor.

5. Book and Web Site Reviews – (about 2 typewritten pages). 6. Extended Abstracts – (about 2 typewritten pages) of Lectures given at

international meetings. 7. Technical Papers – (about 10 typewritten pages) report on applications of an

already described innovation. Typically, technical articles are not based on new experiments.

Reviewing the Manuscript

All contributors are evaluated according to the criteria of originality and quality of their scientific content, and only those deemed worthy will be accepted for publication. To facilitate the reviewing process, authors are encouraged to suggest three persons competent to review their manuscript. Such suggestions will be taken into consideration but not always accepted. The Editor-In-Chief and Editors have the right to decline formal review of a manuscript when it is deemed that the manuscript is:

1. on a topic outside the scope of the Journal; 2. lacking technical merit; 3. of insufficient novelty for a wide international readership; 4. fragmentary and providing marginally incremental results; or 5. is poorly written.

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Proofs When a manuscript is ready for printing, the corresponding author will receive a

PDF-formatted manuscript for proof reading, which should be returned to the journal within one week. Failure to do so will be taken as the authors are in agreement with any alteration which may have occurred during the preparation of the manuscript. Copyright

Subscribers may reproduce tables of contents or prepare lists of articles including abstracts for internal circulation within their institutions. Permission of the Publisher is required for resale or distribution outside the institution and for all other derivative works, including compilations and translations. Professional Ethics and Publication Policy

The journal expects the Editors, Referees and authors to adhere to the well-known standards of professional ethics. Authors are responsible for the factual accuracy of their contributions. Submission of the paper commits the author not to submit the same material elsewhere. Referees should act promptly. If certain circumstances preclude prompt attention to the manuscript at the time it is received, the non-received manuscript should be returned immediately to the Editor or the Referee should contact the Editor for possible delay of the report submission date. The Editor accepts full responsibility for his decisions on the manuscripts. PREPARATION AND SUBMISSION OF MANUSCRIPT Cover Letter

Manuscripts must be accompanied by a cover letter in which the type of the submitted manuscript. It should contain:

1. full name(s) of the author(s), 2. mailing address (address, phone and fax numbers, e-mail) of the author to whom

correspondence should be addressed, 3. title of the paper (concise, without any abbreviations), 4. type of contribution, 5. a statement that the article is original and is currently not under consideration by

any other journal or any other medium, including preprints, electronic journals and computer databases in the public domain, and

6. the names, full affiliation (department, institution, city and country), and 7. e-mail addresses of three potential Referees. Contributors from Bosnia and Herzegovina should provide the name and full

affiliation of at least one Referee from abroad. Authors are fully encouraged to use Cover Letter Template.


Manuscript preparation

The submitted articles must be prepared with Word for Windows. Manuscripts should be typed in English (either standard British or American English, but consistent throughout) with 1.5 spacing (12 points Times New Roman; Greek letters in the character font Symbol) in A4 format leaving 2.5 cm for margins. Authors are fully encouraged to use Manuscript Template.

All contributions should be written in a style that addresses a wider audience than papers in more specialized journals. Manuscripts with grammar or vocabulary deficiencies are disadvantaged during the scientific review process and, even if accepted, may be returned to the author to be rewritten in idiomatic English. The authors are requested to seek the assistance of competent English language expert, if necessary, to ensure their English is of a reasonable standard. The journal maintains its policy and takes the liberty of correcting the English of manuscripts scientifically accepted for publication.

Tables and figures and/or schemes should not be embedded in the manuscript but their position in the text indicated. In electronic version (Word.doc document) tables and figures and/or schemes should follow the text, each on a separate page. Please number all pages of the manuscript including separate lists of references, tables and figures with their captions.

IUPAC and International Union of Biochemistry and Molecular Biology recommendations for the naming of compounds should be followed.

SI units, or other permissible units, should be employed. The designation of physical quantities should be in Times New Roman font. In text, graphs, and tables, brackets should be used to separate the designation of a physical quantity from the unit. Please do not use the axes of graphs for additional explanations; these should be mentioned in the figure captions and/or the manuscript (example: “pressure at the inlet of the system, kPa” should be avoided).

Percents and per mills, although not being units in the same sense as the units of dimensioned quantities, can be treated as such. Unit symbols should never be modified (for instance: w/w %, vol.%, mol.% ) but the quantity measured has to be named, e.g. mass fraction, w=95 %; amount (mole) fraction, x=20 %.

Latin words, as well as the names of species, should be in italic, as for example: i.e., e.g., in vivo, ibid, Artemisia annua L., etc. The branching of organic compound should also be indicated in italic, for example, n-butanol, tert-butanol, etc.

Decimal numbers must have decimal points and not commas in the text (except in the Bosnian/Croatian/Serbian abstract), tables and axis labels in graphical presentations of results. Thousands are separated, if at all, by a comma and not a point. Structure of the Manuscript

The manuscript must contain, each on a separate page, the title page, abstract in English, (abstract in Bosnian/Croatian/Serbian), graphical abstract (optional), main text,

74


list of references, tables (each table separately), illustrations (each separately), and legends to illustrations (all on the same page). 1. Title page must contain: the title of the paper (bold letters), full name(s) of the

author(s), full mailing addresses of all authors (italic), keywords (up to 6), the phone and fax numbers and the e-mail address of the corresponding author.

2. A one-paragraph abstract written of 150–200 words in an impersonal form indicating the aims of the work, the main results and conclusions should be given and clearly set off from the text. Domestic authors should also submit, on a separate page, a Summary/Sažetak. For authors outside Bosnia and Herzegovina, the Editorial Board will provide a Bosnian/Croatian/Serbian translation of their English abstract.

3. Authors are encouraged to submit a graphical abstract that describes the subject matter of the paper. It should contain the title of the paper, full name(s) of the author(s), and graphic that should be no larger than 11 cm wide by 5 cm tall. Authors are fully encouraged to use Graphical Abstract Template.

4. Main text should have the following form: - Introductionshould include the aim of the research and a concise description of

background information and related studies directly connected to the paper. - Experimental section should give the purity and source of all employed

materials, as well as details of the instruments used. The employed methods should be described in sufficient detail to enable experienced persons to repeat them. Standard procedures should be referenced and only modifications described in detail.

- Results and Discussionshould include concisely presented results and their significance discussed and compared to relevant literature data. The results and discussion may be combined or kept separate.

- The inclusion of a Conclusionsection, which briefly summarizes the principal conclusions, is highly recommended.

- Acknowledgement (optional). - Please ensure that every reference cited in the text is also present in the

reference list (and vice versa). Unpublished results and personal communications are not recommended in the reference list, but may be mentioned in the text. If these references are included in the reference list they should follow the standard reference style of the journal and should include a substitution of the publication date with either "Unpublished results" or "Personal communication" Citation of a reference as "in press" implies that the item has been accepted for publication. As a minimum, the full URL should be given and the date when the reference was last accessed. Any further information, if known (DOI, author names, dates, reference to a source publication, etc.), should also be given. No more than 30 references should be cited in your manuscript. In the text refer to the author's name (without initials) and year of publication (e.g. "Steventon, Donald and Gladden (1994) studied the effects..." or "...similar to values reported by others (Anderson, Douglas, Morrison, et al., 1990)..."). Type the names of the first three authors at first citation. At subsequent citations use


first author et al. The list of references should be arranged alphabetically by authors' names and should be as full as possible, listing all authors, the full title of articles and journals, publisher and year. Examples of reference style: a) Reference to a journal publication:

Warren, J. J., Tronic, T. A., Mayer, J. M. (2010). Termochemistry of proton-coupled electron transfer reagents and its implications. Chemical Reviews, 110 (12), 6961-7001.

b) Reference to a book: Corey, E. J., Kurti, L. (2010). Enantioselective chemical synthesis. (1st Ed.) Direct Book Publishing, LLC.

c) Reference to a chapter in an edited book: Moody, J. R., Beck II, C. M. (1997). Sample preparation in analytical chemistry. In Setlle, F. A. (Ed.), Handbook of instrumental techniques for analytical chemistry. (p.p. 55-72). Prentice Hall.

d) Reference to a proceeding: Seliskar, C. J., Heineman, W.R., Shi, Y., Slaterbeck, A.F., Aryal, S., Ridgway, T.H., Nevin, J.H. (1997). New spectroelectrochemical sensor, in Proceedings of 37th Conference of Analytical Chemistry in Energy and Technology, Gatlinburg, Tenesee, USA, p.p. 8-11.

e) Patents: Healey, P.J., Wright, S.M., Viltro, L.J., (2004).Method and apparatus for the selection of oral care chemistry, The Procter & Gamble Company Intellectual Property Division, (No.US 2004/0018475 A1).

f) Chemical Abstracts: Habeger, C. F., Linhart, R. V., Adair, J. H. (1995). Adhesion to model surfaces in a flow through system. Chemical Abstracts, CA 124:25135.

g) Standards: ISO 4790:1992. (2008). Glass-to-glass sealings - Determination of stresses.

h) Websites: Chemical Abstract Service, www.cas.org, (18/12/2010).

- Tables are part of the text but must be given on separate pages, together with their captions. The tables should be numbered consequently in Latin numbers. Quantities should be separated from units by brackets. Footnotes to tables, in size 10 font, are to be indicated consequently (line-by-line) in superscript letters. Tables should be prepared with the aid of the Word table function, without vertical lines. Table columns must not be formatted using multiple spaces. Table rows must not be formatted using Carriage returns (enter key; key). Tables should not be incorporated as graphical objects.

- Figures and/or Schemes (in high resolution) should follow the captions, each on a separate page of the manuscript. High resolution illustrations in TIF or EPS format (JPG format is acceptable for colour and greyscale photos, only) must be uploaded as a separate archived (.zip or .rar) file.

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Figures and/or Schemes should be prepared according to the artwork instructions. - Mathematical and chemical equations must be numbered, Arabic numbers,

consecutively in parenthesis at the end of the line. All equations should be embedded in the text except when they contain graphical elements (tables, figures, schemes and formulae). Complex equations (fractions, inegrals, matrix…) should be prepared with the aid of the Word Equation editor.

Artwork Instructions

Journal accepts only TIF or EPS formats, as well as JPEG format (only for colour and greyscale photographs) for electronic artwork and graphic files. MS files (Word, PowerPoint, Excel, Visio) are NOT acceptable. Generally, scanned instrument data sheets should be avoided. Authors are responsible for the quality of their submitted artwork.

Image quality: keep figures as simple as possible for clarity - avoid unnecessary complexity, colouring and excessive detail. Images should be of sufficient quality for the printed version, i.e. 300 dpi minimum.

Image size: illustrations should be submitted at its final size (8 cm for single column width or 17 cm for double column width) so that neither reduction nor enlargement is required.

Photographs: please provide either high quality digital images (250 dpi resolution) or original prints. Computer print-outs or photocopies will not reproduce well enough for publication. Colour photographs rarely reproduce satisfactorily in black and white.

The facility exist for color reproduction, however the inclusion of color photographs in a paper must be agreed with Editor in advance.

Reporting analytical and spectral data

The following is the recommended style for analytical and spectral data presentation:

1. Melting and boiling points: mp 163–165°C (lit. 166°C) mp 180°C dec. bp 98°C Abbreviations: mp, melting point; bp, boiling point; lit., literature value; dec, decomposition.

2. Specific Rotation: [a]23D –222 (c 0.35, MeOH). Abbreviations: a, specific rotation; D, the sodium D line or wavelength of light used for determination; the superscript number, temperature (°C) at which the determination was made; In parentheses: c stands for concentration; the number following c is the concentration in grams per 100 mL; followed by the solvent name or formula.


3. NMR Spectroscopy: 1H NMR (500 MHz, DMSO-d6) d 0.85 (s, 3H, CH3), 1.28–1.65 (m, 8H, 4´CH2), 4.36–4.55 (m, 2H, H-1 and H-2), 7.41 (d, J 8.2 Hz, 1H, ArH), 7.76 (dd, J 6.0, 8.2 Hz, 1H, H-1'), 8.09 (br s, 1H, NH). 13C NMR (125 MHz, CDCl3) d 12.0, 14.4, 23.7, 26.0, 30.2, 32.5, 40.6 (C-3), 47.4 (C-2'), 79.9, 82.1, 120.0 (C-7), 123.7 (C-5), 126.2 (C-4). Abbreviations: d, chemical shift in parts per million (ppm) downfield from the standard; J, coupling constant in hertz; multiplicities s, singlet; d, doublet; t, triplet; q, quartet; and br, broadened. Detailed peak assignments should not be made unless these are supported by definitive experiments such as isotopic labelling, DEPT, or two-dimensional NMR experiments.

4. IR Spectroscopy: IR (KBr) n 3236, 2957, 2924, 1666, 1528, 1348, 1097, 743 cm–1. Abbreviation: n, wavenumber of maximum absorption peaks in reciprocal centimetres.

5. Mass Spectrometry: MS m/z (relative intensity): 305 (M+H, 100), 128 (25). HRMS–FAB (m/z): [M+H]+calcd for C21H38N4O6, 442.2791; found, 442.2782. Abbreviations: m/z, mass-to-charge ratio; M, molecular weight of the molecule itself; M+, molecular ion; HRMS, high-resolution mass spectrometry; FAB, fast atom bombardment.

6. UV–Visible Spectroscopy: UV (CH3OH) lmax (log e) 220 (3.10), 425 nm (3.26). Abbreviations: lmax, wavelength of maximum absorption in nanometres; e, extinction coefficient.

7. Quantitative analysis: Anal.calcd for C17H24N2O3: C 67.08, H 7.95, N 9.20. Found: C 66.82, H 7.83, N 9.16.All values are given in percentages.

8. Enzymes and catalytic proteins relevant data: Papers reporting enzymes and catalytic proteins relevant data should include the identity of the enzymes/proteins, preparation and criteria of purity, assay conditions, methodology, activity, and any other information relevant to judging the reproducibility of the results1. For more details check Beilstein Institut/STRENDA (standards for reporting enzymology data) commission Web site (http://www.strenda.org/documents.html).

1 For all other data presentation not mentioned above please contact Editor for instructions.

http://www.strenda.org/documents.html

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Submission Checklist The following list will be useful during the final checking of an article prior to

sending it to the journal for review:

• E-mail address for corresponding author, • Full postal address, • Telephone and fax numbers, • All figure captions, • All tables (including title, description, footnotes), • Manuscript has been "spellchecked" and "grammar-checked", • References are in the correct format for the journal, • All references mentioned in the Reference list are cited in the text, and vice versa.

Submissions

Submissions should be directed to the Editor by e-mail: [email protected], or [email protected]. All manuscripts will be acknowledged on receipt (by e-mail) and given a reference number, which should be quoted in all subsequent correspondence.



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